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Tier-1 employer, generalist data engineer title, mid-level experience, metro location, and broad Databricks/Spark skills drive high competition.
Core data engineering skills transfer well, but legal/compliance governance needs require some industry familiarity.
Explicit 5–8 year requirement plus mandatory Python and Databricks/Spark experience and regulated legal data increases strictness.
Design, build, and maintain scalable Databricks-based data pipelines integrating structured and unstructured legal data into enterprise data fabric.
Ensure pipeline reliability, scalability, and performance with production-ready engineering best practices and automated validation.
Collaborate with Legal stakeholders, Data Architects, and AI/Analytics teams to deliver governed datasets supporting analytics, reporting, and AI/ML use cases.
5-8 years of experience in data engineering or related technical role.
Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or related field required.
Strong experience with SQL, Python (mandatory), and Hands-on experience with Databricks / Apache Spark required.
Not explicitly mentioned in the JD: Notice period or strict location requirements.
Experienced in modern data platforms like Databricks with solid understanding of Delta Lake and Spark workload optimization.
Able to work at the intersection of Legal domain data needs and enterprise data strategy, collaborating with multi-disciplinary teams.
Familiar with data governance, data quality controls, and Agile delivery processes in enterprise environments supporting AI/ML and compliance use cases.