Data Engineer

Mid-level, hands-on contributor with a proven track record of delivering tangible outputs-not just oversight.

Demonstrated ability to work independently as a “doer,” jumping into complex tasks and driving them to completion with minimal guidance.

Experience with modern data warehousing platforms.

Snowflake experience strongly preferred.

If not Snowflake, deep hands-on experience with a comparable enterprise cloud data warehouse (e.g., BigQuery, Redshift, Azure Synapse).

A process-oriented mindset, with experience identifying gaps, improving workflows, and implementing repeatable, scalable solutions.

Demonstrated ability to quickly learn new tools, technologies, and patterns, especially in a rapidly evolving data ecosystem.

Strong collaboration skills with the ability to work across teams to refine requirements, identify process improvements, align on delivery expectations and aligning with data engineers, architects, analysts, and product partners.

Python skills with practical experience building data pipelines, automation scripts, integrations, and reusable components.

Ability to write clean, maintainable, testable code following modern engineering best practices.

Should-Have Requirements

Deep understanding of ETL/ELT design principles, including orchestration, dependency management, error handling, and data validation.

Prior experience implementing template-based and configuration-driven frameworks for ETL processes is highly desirable.

Ability to design, optimize, and troubleshoot complex data movement pipelines.

Strong understanding of dimensional modeling, structured data storage, performance tuning, and schema design.

Working-level proficiency with Snowflake, including:

Query performance tuning

Query writing

Grasp of Snowflake features

Could-Have / Nice-to-Have Requirements

Familiarity with DevOps concepts and processes, particularly around:

CI/CD for data engineering

Branching strategies

Deployment automation

Experience with Azure cloud services, especially event-driven or serverless compute such as Azure Functions.

Ability to translate solution architecture blueprints into actionable implementation steps.

Capable of working closely with a lead engineer or architect to break down a strategy and drive execution on individual components.