Data Engineer

About the role

We are 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. The 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 a combination of Snowflake Tasks, ADF jobs, dbt jobs

Typical engineering work includes ingesting and reconciling data across various source systems (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. The role requires strong data‑engineer fundamentals and the ability to engage cross‑functionally with business teams to align BI products with existing needs. The candidate should have full‑stack development expertise, stakeholder engagement experience, and project‑based development skills. This role works closely with the DnA Lead, internal stakeholders, and technical teams in marketing, operations, and finance.

Key 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 bronze, silver, gold, and platinum levels 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 downstream tools that align with data builds based on project and business needs.
• Translate business questions from other departments into reliable and repeatable data models, collaborating with the DnA Lead to scope requirements and inform sequencing.
• Work with data scientists to build models that feed predictive‑modelling efforts.
• Own data quality checks across pipelines and models, including completeness, quality testing, and thresholds.
• Maintain naming conventions, attribute definitions, and contextual documentation for analysis and utilization of data models.
• Maintain incident and root‑cause documentation for pipeline failures, data‑quality regressions, and other bugs to ensure resolution and track incremental improvements.

Skills and Qualifications

• 4+ years of experience in data engineering or analytics engineering, with hands‑on experience building and maintaining production‑level data pipelines.
• Strong SQL proficiency and experience building, maintaining, and optimizing modular logic in dbt.
• Python experience for ingestion logic, data transformation, and 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, including requirement gathering and sprint delivery.
• Strong documentation practices: producing and maintaining model definitions, lineage documentation, and data dictionaries.
• 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 event‑driven data patterns.
• 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.