Sr. Analytics Engineer/ Data Engineer
for each section heading. Use - for any list of duties/requirements/benefits. Use
for prose.
We need to preserve all substantive information; do not invent or summarize away detail. So we need to include all details from the posting, but we can group them logically.
Let's extract content:
Intro paragraph about Laurel (mission etc). That's not part of role but could be considered "About the company". However we only need sections as per instruction; we could put that in a
before sections? The instruction: "Output ONLY the HTML fragment — no /, no markdown code fences." It didn't forbid extra paragraphs outside sections. But they said use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Use - for any list of duties/requirements/benefits, and
for prose. Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it. So we can include an introductory paragraph about Laurel before sections. That's fine.
Now sections:
About the role: The paragraph starting "As a Senior Analytics Engineer/Data Engineer at Laurel, you’ll turn product and business data into clear, trustworthy insights leaders can act on." Continue until maybe before "What You Will Do". That is the description.
Responsibilities: The bullet list under "What You Will Do". Items:
- Build analyses & automation (SQL/Python)
- Run recurring ROI analyses (Business Impact Reports).
- Write performant SQL and pandas; productionize repeatable jobs (scheduling, alerts, anomaly checks) with orchestration (e.g., Airflow).
- Define metrics & model the data
- Own metric definitions (e.g., True Time vs. Released), create reusable SQL Data Models that serve as the analytics source of truth.
- Partner with CX on customer ROI
- Quantify and communicate Laurel’s impact, prepare exec-ready materials, and join customer-facing presentations—translating for both non-technical and technical stakeholders.
- Ship dashboards & actionable insights
- Deliver customer-facing ThoughtSpot dashboards and turn findings into concise actionable insights
- Raise data quality & instrumentation
- Add validation tests and monitoring, triage data issues quickly, and collaborate with Product/Engineering to improve data quality.
- Data Platform Development
- Design, build, and maintain Laurel’s Analytics Data Warehouse as the single source of truth for analytics and reporting needs.
- Create scalable ETL pipelines to ingest, process, and organize data from diverse sources (PMS, Web Analytics, WebApp).
- Deploy and maintain Business Intelligence tools to provide analytics and reporting capabilities.
We need to preserve all details. Could flatten into list items, each maybe a sentence.
Requirements: Education: Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Experience: 3+ years of professional experience as a Data Scientist. Ideal candidates will be comfortable working with large-scale data systems.
Technical Proficiency: Advanced SQL and Python; Experience with data orchestration tools (e.g., Airflow, Prefect, Dagster); Proficiency in modern data warehouses (e.g., Snowflake, BigQuery, Redshift); Familiarity with data modeling, warehousing principles, and BI tools (e.g., Thoughtspot, PowerBI, Tableau); Ability to build ML models and quickly prototype solutions (classification, clustering, regression, NLP) that inform product direction.
Experience With: Cloud platform expertise (AWS, GCP, Azure); Knowledge of dbt, Kubernetes, and Terraform; Exposure to CI/CD pipelines and DevOps practices.
Soft Skills: Strong problem-solving and communication skills; Ability to work in a fast-paced startup environment and manage multiple priorities.
Nice to haves: Experience with knowledge worker productivity tools; Familiarity with modern data visualization libraries (e.g., D3.js); ML and AI: Prototype, build and test ML models to quickly validate hypotheses and generate insights that guide Laurel product features.
We could combine Requirements and Qualifications and Skills sections. But we need to decide which sections to include. The instruction: "Prefer a few substantive sections over many thin ones." So we could combine education/experience into Requirements, technical proficiency into Qualifications, soft skills into Skills, nice-to-haves maybe also Skills or Qualifications. But we must not create sections with filler. We have content for each.
We could have:
- About the role (prose)
- Responsibilities (ul)
- Requirements (ul) covering education, experience, maybe technical proficiency? But technical proficiency could be Qualifications.
- Qualifications (ul) covering technical proficiency, experience with cloud, dbt, etc.
- Skills (ul) covering soft skills and nice-to-haves.
- Benefits (ul) covering compensation benefits.
- Pay (p) covering salary range.
- Schedule (p) covering hybrid 3 days per week.
We must not include placeholder like "commensurate with experience". We have actual numbers.
We must not repeat job title.
Now ensure we preserve all substantive info: The intro paragraph about Laurel mission etc. Also the "Why join Laurel" paragraph includes benefits like equity, 401K, off-sites, etc. That's benefits info; we should include those in Benefits section as well.
Also "We encourage diverse perspectives..." is EEO boilerplate; we should remove that. Also "Laurel is an equal opportunity employer..." remove. Also "We are not able to support visa sponsorship or relocation assistance." That's minor metadata; we could fold into a short p or drop. Probably drop as it's minor.
Also "If you think you'd be a good fit for this role, we encourage you to apply..." that's application instruction; remove.
Also "Compensation Range: $218K - $240K" duplicate; we can mention pay range.
Now craft HTML.
We'll start with an intro p about Laurel mission.
Then
About the Role
...
Then Responsibilities
- ...
...
Then Requirements
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 3+ years of professional experience as a Data Scientist; comfortable working with large-scale data systems.
Then Qualifications
- Advanced SQL and Python.
- Experience with data orchestration tools (e.g., Airflow, Prefect, Dagster).
- Proficiency in modern data warehouses (e.g., Snowflake, BigQuery, Redshift).
- Familiarity with data modeling, warehousing principles, and BI tools (e.g., ThoughtSpot, PowerBI, Tableau).
- Ability to build ML models and quickly prototype solutions (classification, clustering, regression, NLP) that inform product direction.
- Cloud platform expertise (AWS, GCP, Azure).
- Knowledge of dbt, Kubernetes, and Terraform.
- Exposure to CI/CD pipelines and DevOps practices.
Then Skills
- Strong problem-solving and communication skills.
- Ability to work in a fast-paced startup environment and manage multiple priorities.
- Experience with knowledge worker productivity tools (nice-to-have).
- Familiarity with modern data visualization libraries (e.g., D3.js) (nice-to-have).
- ML and AI: Prototype, build and test ML models to quickly validate hypotheses and generate insights that guide Laurel product features (nice-to-have).
Then Benefits
- Competitive salary and generous equity.
- Comprehensive medical, dental, and vision coverage with covered premiums.
- 401(k) plan.
- Wellness, commuter, and FSA stipends.
- Bi-annual in-person company off-sites in unique locations.
- Opportunity to perform at your best while growing and making a meaningful impact.
Then Pay
For candidates based in San Francisco the compensation range for this role is $205,000–$249,000 USD (final compensation based on experience, qualifications, and expertise).
Then Schedule
Hybrid role based in the San Francisco office, 3 days per week; exceptional candidates in other US locations considered case‑by‑case.
We need to ensure we didn't miss any substantive info: The "What You Will Do" list includes "Build analyses & automation (SQL/Python)" etc. We captured.
Also "Define metrics & model the data" includes "Own metric definitions (e.g., True Time vs. Released), create reusable SQL Data Models that serve as the analytics source of truth." We captured under Define metrics & model the data.
Also "Partner with CX on customer ROI" captured.
Also "Ship dashboards & actionable insights" captured.
Also "Raise data quality & instrumentation" captured.
Also "Data Platform Development" captured.
Also "You will be a great fit if you have Education..." captured.
Also "Experience: 3+ years of professional experience as a Data Scientist." captured.
Also "Ideal candidates will be comfortable working with large-scale data systems." captured.
Also "Technical Proficiency:" captured.
Also "Experience With:" captured.
Also "Soft Skills:" captured.
Also "Nice to haves" captured.
Also "Why join Laurel" captured benefits.
Also "Compensation: Competitive salary, generous equity, comprehensive medical/dental/vision coverage with covered premiums, 401(k), additional benefits including wellness/commuter/FSA stipends." captured.
Also "For candidates based in San Francisco the compensation range for this role is $20
for prose. We need to preserve all substantive information; do not invent or summarize away detail. So we need to include all details from the posting, but we can group them logically. Let's extract content: Intro paragraph about Laurel (mission etc). That's not part of role but could be considered "About the company". However we only need sections as per instruction; we could put that in a
before sections? The instruction: "Output ONLY the HTML fragment — no /, no markdown code fences." It didn't forbid extra paragraphs outside sections. But they said use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Use - for any list of duties/requirements/benefits, and
for prose. Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it. So we can include an introductory paragraph about Laurel before sections. That's fine.
Now sections:
About the role: The paragraph starting "As a Senior Analytics Engineer/Data Engineer at Laurel, you’ll turn product and business data into clear, trustworthy insights leaders can act on." Continue until maybe before "What You Will Do". That is the description.
Responsibilities: The bullet list under "What You Will Do". Items:
- Build analyses & automation (SQL/Python)
- Run recurring ROI analyses (Business Impact Reports).
- Write performant SQL and pandas; productionize repeatable jobs (scheduling, alerts, anomaly checks) with orchestration (e.g., Airflow).
- Define metrics & model the data
- Own metric definitions (e.g., True Time vs. Released), create reusable SQL Data Models that serve as the analytics source of truth.
- Partner with CX on customer ROI
- Quantify and communicate Laurel’s impact, prepare exec-ready materials, and join customer-facing presentations—translating for both non-technical and technical stakeholders.
- Ship dashboards & actionable insights
- Deliver customer-facing ThoughtSpot dashboards and turn findings into concise actionable insights
- Raise data quality & instrumentation
- Add validation tests and monitoring, triage data issues quickly, and collaborate with Product/Engineering to improve data quality.
- Data Platform Development
- Design, build, and maintain Laurel’s Analytics Data Warehouse as the single source of truth for analytics and reporting needs.
- Create scalable ETL pipelines to ingest, process, and organize data from diverse sources (PMS, Web Analytics, WebApp).
- Deploy and maintain Business Intelligence tools to provide analytics and reporting capabilities.
We need to preserve all details. Could flatten into list items, each maybe a sentence.
Requirements: Education: Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Experience: 3+ years of professional experience as a Data Scientist. Ideal candidates will be comfortable working with large-scale data systems.
Technical Proficiency: Advanced SQL and Python; Experience with data orchestration tools (e.g., Airflow, Prefect, Dagster); Proficiency in modern data warehouses (e.g., Snowflake, BigQuery, Redshift); Familiarity with data modeling, warehousing principles, and BI tools (e.g., Thoughtspot, PowerBI, Tableau); Ability to build ML models and quickly prototype solutions (classification, clustering, regression, NLP) that inform product direction.
Experience With: Cloud platform expertise (AWS, GCP, Azure); Knowledge of dbt, Kubernetes, and Terraform; Exposure to CI/CD pipelines and DevOps practices.
Soft Skills: Strong problem-solving and communication skills; Ability to work in a fast-paced startup environment and manage multiple priorities.
Nice to haves: Experience with knowledge worker productivity tools; Familiarity with modern data visualization libraries (e.g., D3.js); ML and AI: Prototype, build and test ML models to quickly validate hypotheses and generate insights that guide Laurel product features.
We could combine Requirements and Qualifications and Skills sections. But we need to decide which sections to include. The instruction: "Prefer a few substantive sections over many thin ones." So we could combine education/experience into Requirements, technical proficiency into Qualifications, soft skills into Skills, nice-to-haves maybe also Skills or Qualifications. But we must not create sections with filler. We have content for each.
We could have:
- About the role (prose)
- Responsibilities (ul)
- Requirements (ul) covering education, experience, maybe technical proficiency? But technical proficiency could be Qualifications.
- Qualifications (ul) covering technical proficiency, experience with cloud, dbt, etc.
- Skills (ul) covering soft skills and nice-to-haves.
- Benefits (ul) covering compensation benefits.
- Pay (p) covering salary range.
- Schedule (p) covering hybrid 3 days per week.
We must not include placeholder like "commensurate with experience". We have actual numbers.
We must not repeat job title.
Now ensure we preserve all substantive info: The intro paragraph about Laurel mission etc. Also the "Why join Laurel" paragraph includes benefits like equity, 401K, off-sites, etc. That's benefits info; we should include those in Benefits section as well.
Also "We encourage diverse perspectives..." is EEO boilerplate; we should remove that. Also "Laurel is an equal opportunity employer..." remove. Also "We are not able to support visa sponsorship or relocation assistance." That's minor metadata; we could fold into a short p or drop. Probably drop as it's minor.
Also "If you think you'd be a good fit for this role, we encourage you to apply..." that's application instruction; remove.
Also "Compensation Range: $218K - $240K" duplicate; we can mention pay range.
Now craft HTML.
We'll start with an intro p about Laurel mission.
Then
About the Role
...
Then Responsibilities
- ...
...
Then Requirements
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 3+ years of professional experience as a Data Scientist; comfortable working with large-scale data systems.
Then Qualifications
- Advanced SQL and Python.
- Experience with data orchestration tools (e.g., Airflow, Prefect, Dagster).
- Proficiency in modern data warehouses (e.g., Snowflake, BigQuery, Redshift).
- Familiarity with data modeling, warehousing principles, and BI tools (e.g., ThoughtSpot, PowerBI, Tableau).
- Ability to build ML models and quickly prototype solutions (classification, clustering, regression, NLP) that inform product direction.
- Cloud platform expertise (AWS, GCP, Azure).
- Knowledge of dbt, Kubernetes, and Terraform.
- Exposure to CI/CD pipelines and DevOps practices.
Then Skills
- Strong problem-solving and communication skills.
- Ability to work in a fast-paced startup environment and manage multiple priorities.
- Experience with knowledge worker productivity tools (nice-to-have).
- Familiarity with modern data visualization libraries (e.g., D3.js) (nice-to-have).
- ML and AI: Prototype, build and test ML models to quickly validate hypotheses and generate insights that guide Laurel product features (nice-to-have).
Then Benefits
- Competitive salary and generous equity.
- Comprehensive medical, dental, and vision coverage with covered premiums.
- 401(k) plan.
- Wellness, commuter, and FSA stipends.
- Bi-annual in-person company off-sites in unique locations.
- Opportunity to perform at your best while growing and making a meaningful impact.
Then Pay
For candidates based in San Francisco the compensation range for this role is $205,000–$249,000 USD (final compensation based on experience, qualifications, and expertise).
Then Schedule
Hybrid role based in the San Francisco office, 3 days per week; exceptional candidates in other US locations considered case‑by‑case.
We need to ensure we didn't miss any substantive info: The "What You Will Do" list includes "Build analyses & automation (SQL/Python)" etc. We captured.
Also "Define metrics & model the data" includes "Own metric definitions (e.g., True Time vs. Released), create reusable SQL Data Models that serve as the analytics source of truth." We captured under Define metrics & model the data.
Also "Partner with CX on customer ROI" captured.
Also "Ship dashboards & actionable insights" captured.
Also "Raise data quality & instrumentation" captured.
Also "Data Platform Development" captured.
Also "You will be a great fit if you have Education..." captured.
Also "Experience: 3+ years of professional experience as a Data Scientist." captured.
Also "Ideal candidates will be comfortable working with large-scale data systems." captured.
Also "Technical Proficiency:" captured.
Also "Experience With:" captured.
Also "Soft Skills:" captured.
Also "Nice to haves" captured.
Also "Why join Laurel" captured benefits.
Also "Compensation: Competitive salary, generous equity, comprehensive medical/dental/vision coverage with covered premiums, 401(k), additional benefits including wellness/commuter/FSA stipends." captured.
Also "For candidates based in San Francisco the compensation range for this role is $20
for prose. Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it. So we can include an introductory paragraph about Laurel before sections. That's fine. Now sections: About the role: The paragraph starting "As a Senior Analytics Engineer/Data Engineer at Laurel, you’ll turn product and business data into clear, trustworthy insights leaders can act on." Continue until maybe before "What You Will Do". That is the description. Responsibilities: The bullet list under "What You Will Do". Items: - Build analyses & automation (SQL/Python) - Run recurring ROI analyses (Business Impact Reports). - Write performant SQL and pandas; productionize repeatable jobs (scheduling, alerts, anomaly checks) with orchestration (e.g., Airflow). - Define metrics & model the data - Own metric definitions (e.g., True Time vs. Released), create reusable SQL Data Models that serve as the analytics source of truth. - Partner with CX on customer ROI - Quantify and communicate Laurel’s impact, prepare exec-ready materials, and join customer-facing presentations—translating for both non-technical and technical stakeholders. - Ship dashboards & actionable insights - Deliver customer-facing ThoughtSpot dashboards and turn findings into concise actionable insights - Raise data quality & instrumentation - Add validation tests and monitoring, triage data issues quickly, and collaborate with Product/Engineering to improve data quality. - Data Platform Development - Design, build, and maintain Laurel’s Analytics Data Warehouse as the single source of truth for analytics and reporting needs. - Create scalable ETL pipelines to ingest, process, and organize data from diverse sources (PMS, Web Analytics, WebApp). - Deploy and maintain Business Intelligence tools to provide analytics and reporting capabilities. We need to preserve all details. Could flatten into list items, each maybe a sentence. Requirements: Education: Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience. Experience: 3+ years of professional experience as a Data Scientist. Ideal candidates will be comfortable working with large-scale data systems. Technical Proficiency: Advanced SQL and Python; Experience with data orchestration tools (e.g., Airflow, Prefect, Dagster); Proficiency in modern data warehouses (e.g., Snowflake, BigQuery, Redshift); Familiarity with data modeling, warehousing principles, and BI tools (e.g., Thoughtspot, PowerBI, Tableau); Ability to build ML models and quickly prototype solutions (classification, clustering, regression, NLP) that inform product direction. Experience With: Cloud platform expertise (AWS, GCP, Azure); Knowledge of dbt, Kubernetes, and Terraform; Exposure to CI/CD pipelines and DevOps practices. Soft Skills: Strong problem-solving and communication skills; Ability to work in a fast-paced startup environment and manage multiple priorities. Nice to haves: Experience with knowledge worker productivity tools; Familiarity with modern data visualization libraries (e.g., D3.js); ML and AI: Prototype, build and test ML models to quickly validate hypotheses and generate insights that guide Laurel product features. We could combine Requirements and Qualifications and Skills sections. But we need to decide which sections to include. The instruction: "Prefer a few substantive sections over many thin ones." So we could combine education/experience into Requirements, technical proficiency into Qualifications, soft skills into Skills, nice-to-haves maybe also Skills or Qualifications. But we must not create sections with filler. We have content for each. We could have: - About the role (prose) - Responsibilities (ul) - Requirements (ul) covering education, experience, maybe technical proficiency? But technical proficiency could be Qualifications. - Qualifications (ul) covering technical proficiency, experience with cloud, dbt, etc. - Skills (ul) covering soft skills and nice-to-haves. - Benefits (ul) covering compensation benefits. - Pay (p) covering salary range. - Schedule (p) covering hybrid 3 days per week. We must not include placeholder like "commensurate with experience". We have actual numbers. We must not repeat job title. Now ensure we preserve all substantive info: The intro paragraph about Laurel mission etc. Also the "Why join Laurel" paragraph includes benefits like equity, 401K, off-sites, etc. That's benefits info; we should include those in Benefits section as well. Also "We encourage diverse perspectives..." is EEO boilerplate; we should remove that. Also "Laurel is an equal opportunity employer..." remove. Also "We are not able to support visa sponsorship or relocation assistance." That's minor metadata; we could fold into a short p or drop. Probably drop as it's minor. Also "If you think you'd be a good fit for this role, we encourage you to apply..." that's application instruction; remove. Also "Compensation Range: $218K - $240K" duplicate; we can mention pay range. Now craft HTML. We'll start with an intro p about Laurel mission. Then
About the Role
...
ThenResponsibilities
- ... ...
Requirements
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 3+ years of professional experience as a Data Scientist; comfortable working with large-scale data systems.
Qualifications
- Advanced SQL and Python.
- Experience with data orchestration tools (e.g., Airflow, Prefect, Dagster).
- Proficiency in modern data warehouses (e.g., Snowflake, BigQuery, Redshift).
- Familiarity with data modeling, warehousing principles, and BI tools (e.g., ThoughtSpot, PowerBI, Tableau).
- Ability to build ML models and quickly prototype solutions (classification, clustering, regression, NLP) that inform product direction.
- Cloud platform expertise (AWS, GCP, Azure).
- Knowledge of dbt, Kubernetes, and Terraform.
- Exposure to CI/CD pipelines and DevOps practices.
Skills
- Strong problem-solving and communication skills.
- Ability to work in a fast-paced startup environment and manage multiple priorities.
- Experience with knowledge worker productivity tools (nice-to-have).
- Familiarity with modern data visualization libraries (e.g., D3.js) (nice-to-have).
- ML and AI: Prototype, build and test ML models to quickly validate hypotheses and generate insights that guide Laurel product features (nice-to-have).
Benefits
- Competitive salary and generous equity.
- Comprehensive medical, dental, and vision coverage with covered premiums.
- 401(k) plan.
- Wellness, commuter, and FSA stipends.
- Bi-annual in-person company off-sites in unique locations.
- Opportunity to perform at your best while growing and making a meaningful impact.
Pay
For candidates based in San Francisco the compensation range for this role is $205,000–$249,000 USD (final compensation based on experience, qualifications, and expertise).
ThenSchedule
Hybrid role based in the San Francisco office, 3 days per week; exceptional candidates in other US locations considered case‑by‑case.
We need to ensure we didn't miss any substantive info: The "What You Will Do" list includes "Build analyses & automation (SQL/Python)" etc. We captured. Also "Define metrics & model the data" includes "Own metric definitions (e.g., True Time vs. Released), create reusable SQL Data Models that serve as the analytics source of truth." We captured under Define metrics & model the data. Also "Partner with CX on customer ROI" captured. Also "Ship dashboards & actionable insights" captured. Also "Raise data quality & instrumentation" captured. Also "Data Platform Development" captured. Also "You will be a great fit if you have Education..." captured. Also "Experience: 3+ years of professional experience as a Data Scientist." captured. Also "Ideal candidates will be comfortable working with large-scale data systems." captured. Also "Technical Proficiency:" captured. Also "Experience With:" captured. Also "Soft Skills:" captured. Also "Nice to haves" captured. Also "Why join Laurel" captured benefits. Also "Compensation: Competitive salary, generous equity, comprehensive medical/dental/vision coverage with covered premiums, 401(k), additional benefits including wellness/commuter/FSA stipends." captured. Also "For candidates based in San Francisco the compensation range for this role is $20