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Tier-1 brand, mid-senior generalist data role, metro location, and broad required skillset.
Skills are broadly transferable across industries for data engineering roles.
Explicit 7+ years and mandatory cloud, Spark, Python, and platform experience.
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Architect, design, build, and maintain scalable, high-performance data platforms and pipelines for batch and real-time analytics workloads.
Lead technical initiatives influencing data architecture, engineering best practices, and build trusted data solutions to support analytics, machine learning, and business intelligence.
Collaborate cross-functionally with Data Science, ML Engineering, Product, and Analytics teams; mentor junior engineers and contribute to technical documentation and standards.
Bachelor's degree in Computer Science, IT, Engineering or related field (or equivalent experience).
7+ years of professional experience in Data Engineering, Data Platform Engineering, or Distributed Data Systems.
Hands-on expertise with Python, Spark/PySpark, Advanced SQL, shell scripting, relational (PostgreSQL, MySQL) and NoSQL databases.
Experience with cloud platforms (preferably AWS services like EMR, Glue, S3, Lambda, Redshift), ETL/ELT pipeline design, workflow orchestration (Apache Airflow), and CI/CD implementation.
Senior-level engineer with demonstrated ability to lead and influence data architecture decisions and engineering standards in enterprise-scale data platforms.
Proficient in modern distributed data processing and storage technologies, capable of optimizing pipelines for large-scale analytical workloads including real-time streaming.
Experienced collaborator with cross-functional teams including Product, ML, Analytics to deliver scalable, governed data products enabling machine learning and business insights.