Data Engineer ($130k - $170k)

Data Engineer (Remote, United States)

You’ll be a core individual contributor on the Data & Analytics team, responsible for building and maintaining production ELT pipelines and dbt transformation models that power analytics, reporting, and BI across the organization.

Responsibilities

• Data pipeline development: Build and maintain ELT pipelines using Fivetran and custom integrations.
• Ingestion into the warehouse: Ingest data from source systems (e.g., ERP, Salesforce, project management tools, internal databases) into Snowflake.
• Transformation & modeling: Develop, test, and document dbt models (staging layers, fact/dimension tables) for BI and ad-hoc analysis.
• Data quality & reliability: Create and maintain dbt tests for freshness, accuracy, and consistency; monitor pipelines and perform root-cause resolution.
• Integrations: Add and integrate new sources using APIs and connectors.
• Documentation & enablement: Maintain clear documentation so datasets are reusable and understandable across teams.
• Collaboration: Partner with stakeholders to translate data needs into well-modeled datasets.
• Performance & optimization: Monitor query performance and warehouse utilization; optimize for cost and scalability.
• Engineering standards: Participate in code reviews and follow Git-based workflows with CI/CD.
• AI-assisted development: Use AI coding assistants/agent-based tools to accelerate SQL/dbt development, testing, debugging, and documentation while maintaining production-grade quality.

Requirements (Must-Have)

• 3–5 years experience in data engineering / analytics engineering.
• Hands-on data engineering experience supporting production pipelines.
• Fivetran (or equivalent ELT tools) for ingestion.
• Snowflake (or comparable cloud data warehouse).
• dbt for transformation, testing, and documentation.
• Advanced SQL (CTEs, window functions, complex joins, and optimization).
• Comfort using AI-assisted development tools for SQL/dbt workflows.

Role Fit / Nice-to-Haves

• Experience integrating ERP/Salesforce/project management tools into analytics-ready datasets.
• Demonstrated ownership of data models and influence over engineering standards as the team scales.