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Tier-1 brand, metro location, broad full-stack data requirements create high candidate competition.
Core data engineering skills are transferable across industries, though banking domain familiarity is moderately preferred.
Explicit 8+ years plus mandatory Hadoop/Spark/AWS/Airflow and data modelling skills make filters very strict.
Design and develop scalable, reliable data pipelines and platforms with comprehensive test coverage to support business decision-making.
Collaborate with stakeholders to translate requirements into technical implementations and continuously improve data engineering deliverables.
Provide technical governance and mentoring to team members, ensuring alignment with engineering strategy and successful product delivery.
8+ years experience in Data Engineering within large-scale, data-intensive environments.
Proficiency with Hadoop, Spark, Scala/Python, Hive, SQL, AWS cloud services including EC2, S3, Lambda, Athena, Kinesis, Redshift, Glue, EMR, DynamoDB, and CloudWatch.
Bachelor’s degree in Engineering in Computer Science or Information Technology.
Experience building large/complex data pipelines, data warehousing, data modelling, and ETL/ELT processes; hands-on experience with GitHub and Apache Airflow; Linux/Unix environment familiarity.
Experienced in migration initiatives to AWS Cloud and familiar with AWS certifications related to data engineering or analytics.
Proven ability to design and optimize data pipelines incorporating streaming data and event-driven architectures using tools like Apache Airflow.
Strong domain knowledge in data warehousing, AI/GenAI model implementation, and automation driving business value in large financial or data-intensive organizations.