JD – Data Engineering (ETL) Tech Lead
Experience & Expectations :
Leverage extensive experience (8 to 12 years overall ETL experience, including 2–3 years in a Technology Lead role & 4–6 years in AWS) to drive solution design and delivery.
We are seeking an experienced Tech Lead with strong expertise in Big Data (Spark, Cloudera).
Experience orchestrating complex workflows using AWS Step Functions (state machines) for reliable and scalable data pipelines.
Ability to design end-to-end serverless data architectures integrating Glue, Lambda, S3, and Redshift
Candidates with exposure to drive / assist in architecture, development, and delivery of AI-powered data workflows, leveraging agent-based systems to automate data ingestion, transformation, validation, and orchestration along with modern AI paradigms (Agentic AI, LLMs) to design and lead next-generation intelligent data platforms and ETL pipelines will be preferred.
Core Responsibilities :
Lead and manage the development team by helping them understand the requirements and provide technical guidance
Design, build,



and maintain high volume ETL/ELT pipelines across Hadoop (HDFS, Hive, Spark, Kafka) and AWS (Glue, EMR, Lambda, Step Functions, Redshift).
Develop distributed data processing solutions using PySpark, Spark SQL, and scalable cloud serverless patterns.
Implement reusable data ingestion frameworks for batch, ability to design & implement Orchestration process and Leverage AI
Optimize data workflows using partitioning, bucketing, compression, file formats (Parquet/ORC).
Understanding hybrid data lake architectures using S3 + HDFS, ensuring data governance and best practices are adheres
To lead technical teams and deliver complex projects in an Agile workplace
Design and build the robust, scalable and secure software solutions across the having no/least adoption
Define clear technical specifications and make architecture decisions that align with business goals and long-term scalability.
Implement best prac

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