Description

• Proactively seek clarification when business requirements are ambiguous prior to beginning development work, while exercising independent judgment on technical design decisions.

• Keep work item tickets current in Azure DevOps — updating status, notes, and linked artifacts as work progresses so the team always has an accurate picture.

• Write and maintain complex stored procedures, views, and functions in SQL, following team naming and header standards, and building those standards along the way.

• Conduct peer code reviews with Git-based workflows (Azure DevOps/GitHub); understands that code review means actually reviewing — not rubber-stamping PRs.

• Contribute to and enforce engineering standards (SQL, ADF, Python, Git branching conventions).

What We're Looking For:

• 3–6 years in data engineering, preferably from a technology-first environment.

• Experience with a cloud data warehousing platform (Databricks, Snowflake, or similar); preference to Databricks, but proficiency in one translates readily to another.

• Strong SQL; T-SQL specifically is a plus.

• Python; specifically PySpark and experience with working with dataframes, comfortable with interfacing with APIs

• Experience with Azure Data Factory, parameterization of data sets, pipelines, and focused on building re-usable pipelines.

• Openness to exploring and embracing new technologies, unafraid of automation.

• A low tolerance for repetition and a reflex to automate before complaining

• Clear communicator who can write documentation. Bonus Points

• Experience with CICD / DevOps Release Pipelines, YAML Configurations

• Work with AI/MCP tooling to connect data assets to LLM workflows (you've already done this and will do more of it)

• Web Application and Infrastructure, Networking Experience

• Power BI Reporting

Key Skills

Education

Any Gradute