Senior Data Engineer

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.

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.

• 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.