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Popular mid-level Data Engineer title, broad modern-stack requirements, metro location, and 2-4 year band increase competition.
Core SQL, Python, cloud and modern data stack skills are highly transferable across industries despite consulting context.
Explicit 2-4 year requirement plus many mandatory technical skills and tooling raises shortlisting strictness.
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Design, build, and maintain reliable batch and real-time data pipelines from diverse modern and traditional data sources.
Develop cloud-native ELT/ETL pipelines and lakehouse solutions, deploying to shared-production cloud environments with observability and quality practices.
Prepare clean, well-modeled data to support analytics, dashboards, AI/ML, and GenAI use cases including features for LLM applications.
2-4 years of hands-on data engineering experience with delivered projects.
Strong SQL and Python programming skills with production code delivered.
Hands-on experience with cloud platforms (AWS, Azure, or GCP) and modern data stack tools such as Snowflake, BigQuery, Redshift, Databricks, dbt, Airflow, and Fivetran.
Experience with version control (Git), CI/CD pipelines, and ability to use AI coding assistants (e.g., GitHub Copilot) effectively.
Experienced in consulting roles with ability to deliver independently in client-facing environments.
Strong understanding of data engineering’s role in supporting AI, ML, and GenAI, including feature stores and vector databases for LLM applications.
Capable of collaborating across functions to translate business needs into practical, scalable data solutions, acting as a partner rather than just a developer.