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Mid-level data engineer role with common title but specific Databricks/BigQuery skills yields medium competition.
Platform-specific Databricks and BigQuery expertise moderately limits cross-industry transferability.
Explicit 7–10 years plus mandatory Databricks, BigQuery, pipeline ownership, and leadership makes shortlisting strict.
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Lead the technical direction of the data engineering team, including setting standards, guiding architecture decisions, and reviewing team outputs for quality and scalability.
Design, build, and maintain scalable production data pipelines primarily on Databricks using medallion architecture and optimize workloads on Google BigQuery.
Continuously evaluate and incorporate new features from Databricks and BigQuery platforms to improve pipeline design, governance, and performance.
7-10 years of data engineering experience with proven end-to-end pipeline ownership.
Proven technical leadership experience including architecture decisions, code reviews, and mentorship.
Strong hands-on expertise with Databricks (including medallion architecture) and Google BigQuery platforms.
Proficient in Python, SQL, and Git version control.
Experienced technical leader able to guide and grow an engineering team focused on scalable data pipeline technologies.
Proactive in learning and adopting evolving data engineering technologies, particularly on Databricks and BigQuery ecosystems.
Strong ownership mindset for delivering production-quality pipelines and data models, with ability to collaborate across analytics, data science, and business stakeholders.