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Tier-1 brand, metro location, generalist data-engineer title, broad tech stack and mid-level seniority.
Core data engineering skills are transferable, though financial governance and enterprise tooling add domain specificity.
Explicit 7+ years requirement plus multiple mandatory 4+ year technology and governance skills.
Design, develop, and maintain scalable, reliable, high-performance data pipelines supporting enterprise data products across the organization.
Provide technical leadership for complex data engineering initiatives, mentor junior engineers, and influence architecture and engineering best practices.
Drive optimization of data ingestion, transformation, and publishing processes ensuring adherence to data quality, security, governance, and cost objectives.
7+ years experience designing, building, and delivering enterprise-scale data engineering solutions with demonstrated technical leadership.
4+ years programming experience in Java, Python, or Scala, including PySpark and creating/supporting UDFs and modules like pytest.
4+ years working with big data frameworks including Spark, Hive on Spark, Yarn, and experience with SQL, No-SQL, Transact SQL, and Snowflake implementations.
Experience with data governance, metadata management, deployment and administration on Cloud platforms (preferably AWS or Azure), and CI/CD pipelines for data automation.
Experienced in leading global, cross-functional engineering teams in building complex, scalable data platforms and reusable data engineering frameworks.
Proficient in advanced data pipeline architecture including batch and real-time ingestion and transformation with strong operational focus on performance and reliability.
Skilled in integrating data governance, data quality automation (e.g., Great Expectations), and API design (Swagger/OpenAPI) within enterprise data platforms in a cloud/hybrid environment.