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Well-known employer, metro location, and broad skillset create moderate competition.
Data engineering skills transfer across industries, though BFSI domain knowledge is only desirable, not mandatory.
Explicit 8+ years requirement and many mandatory big-data and Azure technologies make shortlisting highly strict.
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Design, build, and maintain ETL/ELT pipelines on Azure cloud or on-premises for large volumes of structured and unstructured data in batch and real-time processing.
Monitor, optimize, troubleshoot data pipelines to ensure reliability, scalability, performance, and comply with data quality, security, and governance standards.
Mentor data engineers and independently lead design, solutioning, and estimation for data engineering projects while collaborating with cross-functional teams.
8-10+ years of relevant data engineering experience.
Bachelor's degree in computer science, information technology, or equivalent.
Expertise in SQL, Python/Scala, Big Data frameworks (Apache Spark, Hadoop, Hive), Azure cloud services (Data Factory, Eventhub, Synapse, Databricks).
Location: Hybrid role with minimum 3 days per week onsite in office.
Experienced with designing and optimizing batch and real-time data processing pipelines with strong focus on performance tuning and data quality validation.
Skilled in end-to-end data solutioning and estimation along with strong mentorship and stakeholder engagement capabilities.
Comfortable working in complex environments integrating multiple data domains such as data governance, data architecture, and BI insights, preferably with some BFSI domain exposure.