Lead Data Engineer

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real content. We need to parse the job posting. Key parts: - The Opportunity: description of team, role, initial focus. Could be "About the role" or "Overview". We'll use "About the role" heading. - What you'll Do: responsibilities list. Use

Responsibilities. - What you need to succeed: qualifications, requirements, skills. Could split into Requirements and Skills. But we can combine into Requirements (including education, experience) and Skills (technical). However we need to follow instruction: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We can choose appropriate. We have content: BS/MS in Computer Science or equivalent experience, with 8+ years of data engineering experience. Proven expertise in SQL, Python, Spark, and data pipeline orchestration tools (e.g., Airflow, dbt). Hands-on experience with cloud platforms (AWS, Azure, GCP) and cloud-based analytics environments such as Databricks. Practical, hands-on experience applying AI coding assistants (Claude Code, Cursor, GitHub Copilot, or similar) in real engineering tasks — not merely casual experimentation. Comfortable evaluating, and where useful building, lightweight automated or intelligent agents to handle repetitive engineering tasks such as monitoring, alerting, or root cause analysis. Experience working alongside data scientists and BI engineers to provide actionable insights. Excellent written and verbal communication skills, with the ability to explain technical concepts to non-technical collaborators. Genuine interest in data-driven development and a track record of shipping reliable data products. Background supporting agile marketing or growth teams is a plus. Experience working with B2B marketing or sales data, such as account-based marketing, campaign attribution, CRM, or marketing automation platforms, is highly valuable. This role initially focuses on B2B, and having that background is helpful. Over time, the position will expand beyond B2B. We can put these under Requirements (education, experience) and Skills (technical). Or combine. Also we have Pay: Expected Pay Range: $139,000 - $257,550 annually, with California range $177,900 - $257,550. So we can include Pay section. Benefits: Not explicitly listed; there is mention of Adobe benefits but not specific. The posting includes generic Adobe culture but not benefits details. We should not create Benefits section unless actual content. There's no explicit benefits list. So omit Benefits. Schedule: Not mentioned. Omit. Qualifications: Could be separate from Requirements? Might be redundant. We'll include Requirements (education, experience) and Skills (technical). Or we could have Qualifications heading for education/experience and Skills for technical. Let's do both. Thus sections: - About the role (overview) - Responsibilities - Requirements (education, experience, B2B background) - Skills (technical proficiencies) - Pay We need to preserve all substantive information, not invent or summarize away detail. Use

• for any list of duties/requirements/benefits. Use

  for prose. We need to remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions. Also remove Adobe description about empowerment etc? That's generic but maybe considered boilerplate? It's about Adobe mission; could be considered fluff but it's substantive? It's generic but still part of posting. The instruction: Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The Adobe mission paragraph is not exactly boilerplate but could be considered generic. However it's not EEO etc. Might be okay to keep? But we should focus on substantive role info. The instruction says "Prefer a few substantive sections over many thin ones." The Adobe mission paragraph is not directly about role; could be considered filler. Safer to omit it as it's not specific to role. Also the "About Adobe" paragraph is generic. We'll omit. Also remove AI Use Guidelines for Interviews, State-Specific Notices, etc. Those are legal boilerplate. Remove. Thus we keep: - The Opportunity paragraph (first paragraph) - What you'll Do (list of responsibilities) - What you need to succeed (list of qualifications/skills) - Expected Pay Range (including California note) We need to format. Let's extract The Opportunity text: "The Marketing Operations and Engineering team in Growth Marketing and Insights (GMI) handles data architecture, transformation, and engineering for GMI under the Marketing org. We manage backend data for marketing measurement tools and processes such as attribution, product and customer segmentation, and lifetime value. We're looking for a Lead Data Engineer to build and own the data foundation behind Adobe's marketing measurement platform. You will collaborate with business analysts and data scientists to define requirements for new and existing models. You will build data pipelines, improve ETL processes, maintain data quality, and seek ways to boost efficiency and automation in daily tasks. You will oversee the data platform behind Adobe’s marketing models and other marketing measurement and reporting tools used company-wide. The initial focus of this role is Adobe’s B2B marketing and sales data, though the scope is encouraged to grow beyond B2B as the team’s remit expands." That's the overview. Now Responsibilities: "What you'll Do" section bullet points (though not bullet in original but sentences). We'll convert to list. List items: - Provide support in data engineering, building data assets, and software applications. - Provide technical direction and system architecture advice for individual initiatives. - Collaborate with key partners and apply technical proficiency across different stages of the software development process, including requirements elicitation, application architecture definition, and build. - Play a meaningful role in composing high-level design artifacts. - Deliver high-quality code for a module, lead validation across all types of testing, and support activities related to implementation and transition. - Assume responsibility for restructuring the database to better support data scientists and business analysts across the marketing organization. - Scope requirements, connect data sources, and build ETL processes to support new products and routes to market as the team expands campaign measurement products and models for Adobe’s B2C and B2B business units. - Collaborate with Marketing, Finance, and Sales groups to ensure data accuracy and conduct root cause analysis when inconsistencies occur in the data. - Incorporate AI coding tools like Claude Code, Cursor, or GitHub Copilot into the development process. These tools help scaffold pipelines, generate tests, and speed up code reviews. You will still be responsible for the quality and accuracy of all production code. - Find opportunities to automate recurring engineering work — pipeline monitoring, failure triage, documentation — using scripted workflows or AI agents where they genuinely save the team time. We need to keep all detail. Now Requirements (education/experience): - BS/MS in Computer Science or equivalent experience, with 8+ years of data engineering experience. - Background supporting agile marketing or growth teams is a plus. - Experience working with B2B marketing or sales data, such as account-based marketing, campaign attribution, CRM, or marketing automation platforms, is highly valuable. This role initially focuses on B2B, and having that background is helpful. Over time, the position will expand beyond B2B. We could also include "Proven expertise in SQL, Python, Spark, and data pipeline orchestration tools (e.g., Airflow, dbt)." but that's skill. Now Skills: - Proven expertise in SQL, Python, Spark, and data pipeline orchestration tools (e.g., Airflow, dbt). - Hands-on experience with cloud platforms (AWS, Azure, GCP) and cloud-based analytics environments such as Databricks. - Practical, hands-on experience applying AI coding assistants (Claude Code, Cursor, GitHub Copilot, or similar) in real engineering tasks — not merely casual experimentation. - Comfortable evaluating, and where useful building, lightweight automated or intelligent agents to handle repetitive engineering tasks such as monitoring, alerting, or root cause analysis. - Experience working alongside data scientists and BI engineers to provide actionable insights. - Excellent written and verbal communication skills, with the ability to explain technical concepts to non-technical collaborators. - Genuine interest in data-driven development and a track record of shipping reliable data products. Now Pay: - Expected Pay Range: $139,000 - $257,550 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process. In California, the pay range for this position is $177,900 - $257,550. We'll put that in a

  . Now we need to output HTML fragment only, no /. Use

  for each section heading. Use

  for prose paragraphs. Use

  • for lists. We need to decide which content goes in

    vs

    . The overview (The Opportunity) is prose ->

    . Responsibilities list ->

    . Requirements list ->
    . Skills list ->
    . Pay ->

    . We should not repeat job title as heading. Not include Job ID. Now produce HTML. Let's craft:

    About the role

    The Marketing Operations and Engineering team in Growth Marketing and Insights (GMI) handles data architecture, transformation, and engineering for GMI under the Marketing org. We manage backend data for marketing measurement tools and processes such as attribution, product and customer segmentation, and lifetime value. We're looking for a Lead Data Engineer to build and own the data foundation behind Adobe's marketing measurement platform. You will collaborate with business analysts and data scientists to define requirements for new and existing models. You will build data pipelines, improve ETL processes, maintain data quality, and seek ways to boost efficiency and automation in daily tasks. You will oversee the data platform behind Adobe’s marketing models and other marketing measurement and reporting tools used company-wide. The initial focus of this role is Adobe’s B2B marketing and sales data, though the scope is encouraged to grow beyond B2B as the team’s remit expands.

    Responsibilities

    • Provide support in data engineering, building data assets, and software applications.
    • Provide technical direction and system architecture advice for individual initiatives.
    • Collaborate with key partners and apply technical proficiency across different stages of the software development process, including requirements elicitation, application architecture definition, and build.
    • Play a meaningful role in composing high-level design artifacts.
    • Deliver high-quality code for a module, lead validation across all types of testing, and support activities related to implementation and transition.
    • Assume responsibility for restructuring the database to better support data scientists and business analysts across the marketing organization.
    • Scope requirements, connect data sources, and build ETL processes to support new products and routes to market as the team expands campaign measurement products and models for Adobe’s B2C and B2B business units.
    • Collaborate with Marketing, Finance, and Sales groups to ensure data accuracy and conduct root cause analysis when inconsistencies occur in the data.
    • Incorporate AI coding tools like Claude Code, Cursor, or GitHub Copilot into the development process. These tools help scaffold pipelines, generate tests, and speed up code reviews. You will still be responsible for the quality and accuracy of all production code.
    • Find opportunities to automate recurring engineering work — pipeline monitoring, failure triage, documentation — using scripted workflows or AI agents where they genuinely save the team time.

    Requirements

    • BS/MS in Computer Science or equivalent experience, with 8+ years of data engineering experience.
    • Background supporting agile marketing or growth teams is a plus.
    • Experience working with