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Mid-level data engineer at a known multinational, metro location and generalist data skillset increases applicant competition.
Data engineering skills are broadly transferable, though legal/compliance data handling adds moderate domain sensitivity.
Explicit 5–8 years plus mandatory Databricks, Spark, Python and cloud skills create high shortlisting strictness.
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Design, build, and maintain scalable data pipelines integrating data from legal systems into enterprise data fabric to support Legal and Compliance analytics, reporting, and AI/ML use cases.
Develop and optimize ETL/ELT pipelines using Databricks, Spark, and modern frameworks ensuring reliability, scalability, and performance.
Implement data quality, governance, lineage, and security controls for sensitive legal data complying with regulatory requirements.
Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or related field.
5-8 years of experience in data engineering or related technical role.
Strong hands-on experience with SQL, Python, Databricks / Apache Spark, and building ETL/ELT pipelines for large-scale datasets.
Familiarity with cloud platforms such as AWS, Azure, or GCP.
Experienced in working with legal or compliance data domains focusing on data governance, privacy, and sensitive data handling.
Skilled in modern data platform technologies like Databricks, Delta Lake, Spark, and data fabric architectures aligned with enterprise-scale solutions.
Capable of collaborating with Legal stakeholders, Data Architects, and AI teams in an Agile environment to deliver robust data engineering solutions.