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Known employer, mid-level generalist title, 5+ years, and metro location amplify candidate competition.
Core data-engineering skills are transferable but Databricks/Kafka emphasis requires moderate domain-specific fit.
Multiple mandatory technologies and production ownership (Databricks, PySpark, Kafka, APIs) make filters strict.
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Design, build, and maintain end-to-end data products on Databricks encompassing ingestion, storage, transformation, APIs, and user-facing components.
Develop and operate batch, incremental, and streaming data pipelines using SQL, Python, PySpark, Kafka, and Databricks-native tools with a focus on scalability and reliability.
Partner directly with business and technical stakeholders to translate ambiguous needs into technical requirements, rapidly prototype solutions, and deliver secure, scalable data products with full lifecycle ownership.
5+ years of experience in data engineering, software engineering, or related role with ownership of production data solutions.
Strong proficiency in SQL, Python, PySpark; hands-on experience with Databricks, Apache Spark, Delta Lake, and Unity Catalog or similar data governance tools.
Experience designing and supporting streaming or near-real-time data pipelines using Kafka, Kinesis, Event Hubs, or similar technologies.
Experience building REST APIs and backend services using Python frameworks such as FastAPI or Flask; familiarity with AWS, Azure, or GCP cloud-native architecture.
Experienced full-stack data engineer comfortable bridging data engineering, analytics, software engineering, and business teams to produce trusted and actionable data products.
Demonstrated ability to handle ambiguity and work closely with stakeholders to convert business needs into practical, scalable technical solutions.
Technical leader capable of mentoring engineers, leading design/code reviews, and contributing to engineering best practices in a production environment.