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Tier-1 employer, common Data Engineer role, metro location, and broad Databricks/Spark requirements raise competition.
Databricks and Spark skills transfer across industries, but enterprise data fabric and governance require moderate domain fit.
Explicit 8–12 years requirement plus mandatory Databricks, PySpark, and governance skills makes shortlisting strict.
Design, develop, and maintain scalable Databricks pipelines for processing structured, semi-structured, and unstructured data across the Enterprise Data Fabric.
Implement and optimize real-time and batch big data processing solutions using Apache Spark to ensure high availability and cost efficiency.
Develop and maintain CI/CD pipelines for automated data pipeline deployment, version control, and monitoring while ensuring data security, compliance, and governance integration.
8 to 12 years of experience in Computer Science, IT, or related fields with hands-on data engineering experience.
Strong skills in Databricks (Delta Lake, Spark, notebooks), PySpark, SQL, and big data performance tuning.
Experience with enterprise-wide data architectures such as Data Fabric or Data Mesh.
Experience with Scaled Agile Framework (SAFe), Agile delivery, and DevOps practices.
Technically strong with leadership experience and the ability to lead data engineering teams, evidenced by advanced programming and architectural responsibilities.
Experienced in cross-functional collaboration aligning data engineering solutions with enterprise goals and governance frameworks.
Familiar with modern data engineering certifications and a strategic understanding of emerging data technologies such as data virtualization, streaming data, and AI/ML data pipelines.