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Strong employer brand, popular Data Engineer title, mid-level experience band, and broad tech stack increase competition.
Core ETL, SQL, Spark and data-platform skills transfer across industries, though platform-specific experience narrows fit somewhat.
Explicit 4+ years plus mandatory Databricks/dbt/Airflow, governance and observability requirements make filters highly strict.
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Build and maintain scalable, high-quality ELT data pipelines and models using technologies like Airflow, DBT, Databricks, and BI tools (Looker, Sigma).
Lead enterprise-wide data modeling strategies and establish data governance and observability practices to ensure transparent and well-governed data systems.
Collaborate cross-functionally with data scientists, product engineers, and business stakeholders to connect data with business outcomes and drive data literacy and enablement initiatives.
Minimum 4 years experience in data/analytics engineering focusing on data architecture, pipelines, and reporting.
Strong expertise with relational databases, SQL, and DRY data modeling, plus hands-on experience with at least some of the following: AWS, Databricks, Delta Lake, Airflow, dbt, Redshift, Datahub (Databricks and dbt preferred).
Experience implementing enterprise-level data observability, quality, and monitoring frameworks/tools (e.g., dbt tests, Great Expectations, Datadog).
Work Experience Required: Minimum 4+ years in data/analytics engineering.
Experienced with cross-functional collaboration translating ambiguous business needs into robust data solutions, especially partnering with product/backend engineering and business analytics teams.
Proven ability to lead and mentor teams to elevate data culture, preferably in distributed or remote environments.
Ability to clearly communicate technical concepts to leadership and craft narratives linking data trends to business impact.