Senior Data Engineer

We need to output HTML fragment with sections: About the role? The posting includes headings: Job Summary, Job Responsibilities, Job Requirements, Preferred Qualifications, Essential Job Function (which is EEO boilerplate). We must not repeat job title. Use

for section headings. Use

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

  for prose. Remove boilerplate: EEO, equal-opportunity, etc. Also remove navigation, cookie, legal, etc. Also remove duplicated lines. Also we must not create sections for Pay/Schedule/Benefits unless present. Not present. So we can have sections: About the role? The posting includes "Job Summary" which is about the role. Could use heading "About the role". Or we could use "Job Summary" as heading but rule says use

  for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we should map to those canonical headings. Use "About the role" for job summary. Use "Responsibilities" for Job Responsibilities. Use "Requirements" for Job Requirements. Use "Qualifications" for Preferred Qualifications. Possibly also "Skills" if we want to list skills but not required. We'll include only sections with substantive content. We need to preserve all substantive information, not invent or summarize away detail. So we need to include all details from Job Summary, Responsibilities, Requirements, Preferred Qualifications. Must not drop any detail. We need to format lists appropriately. For prose, use

  . For list items, use

  • . We need to avoid repeating job title. No Job ID. We need to drop EEO boilerplate (Essential Job Function). So we omit that section entirely. We need to ensure we don't include filler like "commensurate with experience". Not present. Let's extract content. Job Summary paragraph: "Moultrie is looking for a Data Engineer to join the growing Data and Analytics Team. This team owns the development of insights from extraction that power decision-making across Moultrie. This role will contribute to maintaining and building upon the foundational data layer in Snowflake. The current engineering tech stack includes: Extraction/Ingestion: Fivetran, Azure Data Factory, APIs, Webscraping Transformation: dbt and Snowflake Loading/Integration: API, Cloud Data Ingestion Orchestration: Airflow is being evaluated, current stack is combination of Snowflake Tasks, ADF jobs, dbt jobs Typical engineering work includes ingesting and reconciling data across various source systems (such as telemetry logs, customer and subscription records, transactional data) and building logic in line with medallion architecture to produce models that can be integrated into downstream systems. Work will require strong data engineer fundamentals and the ability to engage cross-functionally with other business teams to align BI products with existing needs. The ideal candidate has expertise in full stack development and experience with stakeholder engagement and project-based development. This role works closely with the DnA Lead, internal stakeholders across the business, and technical teams in marketing, operations, and finance that own department specific tools that depend on data for activation." We need to preserve all that. Could break into

    for prose, and maybe a

    for the tech stack list? The tech stack includes items separated by colons and commas. It's a list: Extraction/Ingestion: Fivetran, Azure Data Factory, APIs, Webscraping; Transformation: dbt and Snowflake; Loading/Integration: API, Cloud Data Ingestion; Orchestration: Airflow is being evaluated, current stack is combination of Snowflake Tasks, ADF jobs, dbt jobs. Could present as
    • each bullet. But we need to preserve detail. We'll create a
      with items. Job Responsibilities: list of bullet points. Let's extract each responsibility line: - Data Modelling and Pipeline Management - Design, build, and maintain pipelines that move data from source systems into Snowflake with documented ingestion patterns and alert systems for failures. - Model data at a bronze, silver, gold, and platinum level in dbt with appropriate tests and documentation. - Build and maintain semantic models that translate datasets for natural language querying. - Contribute to the evaluation and selection of which downstream tools align best with the data build based on project and business need. - Translate business questions from other departments into reliable and repeatable data models, working with DnA Lead to scope requirements and inform sequencing. - Work with the data scientist to build models that feed predictive modelling efforts. - Data Quality and Governance - Own data quality checks across the pipelines and models built including completeness, quality testing, and thresholds. - Inform and maintain naming conventions, attribute definitions, and other context appropriate for analysis and utilization of data models for business stakeholders. - Maintain incident and root cause documentation for pipeline failures, data quality regressions, and other bugs to ensure resolution and track incremental improvements. We can keep as a single list under Responsibilities, or separate subheadings? The rule says use

      for section headings, and

      • for any list of duties/requirements/benefits. So we can have a single
        with all responsibility items. Might be okay. Job Requirements: list: - 4+ years in data engineering or analytics engineering with hands-on experience building and maintaining production level data pipelines. - Strong SQL proficiency: Experience building and maintaining, and optimizing modular logic in dbt. - Python experience: Proficiency with using python for ingestion logic, data transformation when necessary, and other automation processes such as webscraping. - Experience with Git and standard software development practices: version control, code reviews, branching, and CI/CD basics. - Ability to take an ambiguous business problem and work backwards to produce models that support effective solutions by creating a list of requirements and working through sprints to deliver. - Strong documentation practices: able to produce and maintain model definitions, lineage documentation, and data dictionaries that enable other developers and business stakeholders. - Collaborative working style: comfortable operating at the boundary between data engineering, analytics, and business teams. Preferred Qualifications: - Experience in retail, CPG, or consumer hardware. - Familiarity with streaming or even-driven data patterns. This will become more important and the Data and Analytics function grows. - Experience with Tableau, Streamlit, or other tools to facilitate development with business analysts. - Exposure to machine learning workflows and feature store development to enable data science workflows. We need to preserve all that. Now produce HTML fragment. We'll start with

        About the role

        then

        with the job summary prose. But the job summary includes a list of tech stack; we could embed that as

        inside the

        ? Better to separate: after the prose, we can have a

        for tech stack. However the rule says use
        • for any list of duties/requirements/benefits. Tech stack is not duties/requirements/benefits but it's still a list. It's okay to use
          for any list. We'll do that. Thus:

          About the role

          Moultrie is looking for a Data Engineer to join the growing Data and Analytics Team. This team owns the development of insights from extraction that power decision-making across Moultrie. This role will contribute to maintaining and building upon the foundational data layer in Snowflake. Typical engineering work includes ingesting and reconciling data across various source systems (such as telemetry logs, customer and subscription records, transactional data) and building logic in line with medallion architecture to produce models that can be integrated into downstream systems. Work will require strong data engineer fundamentals and the ability to engage cross-functionally with other business teams to align BI products with existing needs. The ideal candidate has expertise in full stack development and experience with stakeholder engagement and project-based development. This role works closely with the DnA Lead, internal stakeholders across the business, and technical teams in marketing, operations, and finance that own department specific tools that depend on data for activation.

          Tech Stack

          but we don't have a canonical heading for tech stack; we could incorporate into About the role as a list. But the rule says prefer a few substantive sections over many thin ones. Could we include tech stack as part of About the role paragraph? Might be okay to embed as a
          after the paragraph but without a heading? That would be a list without heading, which might be okay but maybe better to include a heading like "Tech Stack". However the rule only gave examples of headings; not prohibited to use other headings? It says use

          for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It implies we should use those headings for sections. It doesn't say we cannot use other headings, but to follow the instruction, we should map content to those headings. Tech stack could be part of About the role. We'll include it as a

          inside the About the role section after the prose, without a separate heading. That's acceptable. Thus:

          About the role

          Moultrie is looking for a Data Engineer to join the growing Data and Analytics Team. This team owns the development of insights from extraction that power decision-making across Moultrie. This role will contribute to maintaining and building upon the foundational data layer in Snowflake. Typical engineering work includes ingesting and reconciling data across various source systems (such as telemetry logs, customer and subscription records, transactional data) and building logic in line with medallion architecture to produce models that can be integrated into downstream systems. Work will require strong data engineer fundamentals and the ability to engage cross-functionally with other business teams to align BI products with existing needs. The ideal candidate has expertise in full stack development and experience with stakeholder engagement and project-based development. This role works closely with the DnA Lead, internal stakeholders across the business, and technical teams in marketing, operations, and finance that own department specific tools that depend on data for activation.

          • Extraction/Ingestion: Fivetran, Azure Data Factory, APIs, Webscraping
          • Transformation: dbt and Snowflake
          • Loading/Integration: API, Cloud Data Ingestion
          • Orchestration: Airflow is being evaluated, current stack is combination of Snowflake Tasks, ADF jobs, dbt jobs
          Then Responsibilities:

          Responsibilities

          • Design, build, and maintain pipelines that move data from source systems into Snowflake with documented ingestion patterns and alert systems for failures.
          • Model data at a bronze, silver, gold, and platinum level in dbt with appropriate tests and documentation.
          • Build and maintain semantic models that translate datasets for natural language querying.
          • Contribute to the evaluation and selection of which downstream tools align best with the data build based on project and business need.
          • Translate business questions from other departments into reliable and repeatable data models, working with DnA Lead to scope requirements and inform sequencing.
          • Work with the data scientist to build models that feed predictive modelling efforts.
          • Own data quality checks across the pipelines and models built including completeness, quality testing, and thresholds.
          • Inform and maintain naming conventions, attribute definitions, and other context appropriate for analysis and utilization of data models for business stakeholders.
          • Maintain incident and root cause documentation for pipeline failures, data quality regressions, and other bugs to ensure resolution and track incremental improvements.
          Then Requirements:

          Requirements

          • 4+ years in data engineering or analytics engineering with hands-on experience building and maintaining production level data pipelines.
          • Strong SQL proficiency: Experience building and maintaining, and optimizing modular logic in dbt.
          • Python experience: Proficiency with using python for ingestion logic,