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Tier-1 brand, common Data Engineer title, and metro location drive high applicant competition.
Strong technical data and cloud skills transfer well, but GenAI and Databricks requirements increase domain specificity.
Mandatory 7+ years and specific Databricks, Spark, Kafka, cloud and GenAI requirements make shortlisting stringent.
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Design, build, and optimize scalable distributed data processing systems and ETL/ELT pipelines on Databricks.
Engineer high-quality datasets and implement retrieval architectures to integrate generative AI capabilities.
Develop backend services exposing data products and mentor junior engineers in technical best practices.
7+ years of professional software engineering experience.
Bachelor's degree in Computer Science or related field.
Expert-level skills with Apache Spark, Hadoop, Kafka, Databricks/Delta Lake, Python ecosystem (PySpark, Pandas, NumPy), and advanced AWS or Azure deployment experience.
Not explicitly mentioned in the JD: notice period or strict onsite/location requirements.
Strong blend of traditional big data engineering and modern generative AI infrastructure experience.
Proven experience delivering production-grade data pipelines and back-end data services in cloud environments.
Experienced in mentoring engineers and driving technical best practices within teams.