Data Engineer

Key Responsibilities

● Analyze engineering, operational, and productivity data to uncover trends, risks, and opportunities

● Design and implement data models that improve accessibility, structure, and long-term maintainability of engineering metrics.

● Build or enhance ETL pipelines to collect, transform, and export data from various systems (e.g., GitHub, Jira, Security Scans, Costs tools).

● Partner with stakeholders to define meaningful KPIs across engineering domains (e.g., reliability, security, velocity).

● Explore and implement GenAI tooling to support automation, summarization, and pattern detection in engineering workflows.

● Maintain data hygiene and enforce best practices in data governance and lineage within the API Engineering environment.

Required Skills and Experience

● Proven experience as a Data Analyst or Data Engineer, preferably in a software engineering or DevOps context.

● Strong SQL skills and experience with Python or another scripting language for data transformation and analysis.

● Hands-on experience working with APIs and integrating data across SaaS tools (e.g., Jira, GitHub, Datadog).

● Familiarity with dashboarding/visualization platforms like Looker, Grafana or Tableau.

Demonstrated experience structuring unorganized or siloed data into actionable reporting models.

Desirable:

● Experience designing and building ETL pipelines and data lakes or warehouses (e.g. Snowflake).

● Exposure to GenAI tooling and experience applying AI to engineering or operational workflows.

● Knowledge of modern data orchestration tools (e.g., Airflow, dbt)