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Mid-level, metro location, popular Data Engineer title, broad cloud/stack requirements increase applicant competition.
Core data engineering skills (Python, SQL, Snowflake, cloud) are highly transferable across industries.
Explicit 5+ years and mandatory Python, Snowflake, cloud, CI/CD requirements make shortlisting strict.
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Design, build, maintain, and optimize batch or real-time enterprise-grade data pipelines using cloud technologies (AWS, Snowflake, Databricks).
Collaborate with product owners, data scientists, and analysts to translate business requirements into data solutions aligning with long-term architecture and scale.
Provide technical leadership on data pipeline processes, data warehouse architecture, and mentoring other engineers to follow best practices including monitoring data pipeline performance and quality.
5+ years of enterprise data engineering experience with proficiency in Python and at least one of SQL, Java, R, or Spark.
Hands-on experience with cloud data platforms and tools such as AWS, Snowflake, Databricks, Azure, Glue, Airflow.
Strong knowledge of data integration, replication, and data masking techniques.
Must demonstrate communication skills suitable for interaction with end users and business leaders.
Experienced in leading complex data engineering projects within an agile scrum environment at enterprise scale.
Technical thought leader with ability to design scalable, efficient data pipelines and influence engineering practices across teams.
Comfortable working in cross-functional teams interfacing with architects, product owners, and analysts to deliver data solutions aligned to strategic goals.