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Mid-level, popular data engineering role with broad skill requirements increases applicant competition.
Core data engineering skills (Python, pipelines, cloud warehouses) are broadly transferable across industries.
Mandatory 5+ years plus extensive required technologies (Spark, cloud warehouses, Terraform, CI/CD) enforces strict filters.
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Own end-to-end design, development, and management of scalable, reliable production data pipelines and workflows.
Optimize cloud data platforms and distributed processing systems (e.g., Spark, Apache Beam, Databricks) for high performance and cost efficiency.
Collaborate with engineering, analytics, and AI teams to deliver impactful data products including support for AI/ML workflows such as LLMs, NLP, and MLOps.
5+ years of hands-on experience in data engineering with production-grade pipelines.
Strong expertise in Python, cloud platforms (AWS, Azure, or Google Cloud), and cloud data warehouses (Snowflake, BigQuery, Redshift, Synapse).
Experience with distributed data processing frameworks (Spark, Apache Beam, Databricks) and Infrastructure as Code tools (Terraform, CloudFormation, ARM, Bicep).
Skill in CI/CD workflows, Docker, Git, automated testing, and data quality/reliability best practices.
Technically proficient senior engineer with demonstrated ability to design scalable, performant data architectures in cloud environments.
Experienced in operationalizing AI/ML data workflows, including NLP, LLMs, vector search, and associated MLOps processes.
Comfortable working cross-functionally with analytics, engineering, and AI teams to integrate data solutions effectively into business products.