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Senior, specialized data-architect role across metro locations reduces broad applicant density.
Mandatory BFSI domain knowledge and specialized data/LLM/AWS skills limit cross-industry transferability.
Strict 12+ years requirement plus mandatory BFSI, AWS, Spark and LLM expertise narrows shortlist significantly.
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Design and develop scalable data engineering solutions using Python, Spark, and SQL, focusing on high-volume data processing.
Architect and optimize end-to-end data pipelines on AWS, ensuring reliability, performance, and cost efficiency, aligned with BFSI domain requirements.
Lead development frameworks, conduct code reviews, performance tuning, and provide mentorship while ensuring alignment with security, compliance, and data quality standards.
12-15 years of experience as Data Engineer or similar technical role with solution design/architecture exposure.
Hands-on expertise in Python, Apache Spark (PySpark preferred), and strong SQL skills.
Solid experience with AWS services (S3, Glue, Lambda, EMR, EC2, RDS, IAM, CloudWatch, Airflow, Step Functions) and cloud observability tools (AWS CloudWatch, CloudTrail).
Mandatory BFSI domain knowledge and experience designing data workflows; experience with Databricks is good to have.
Experienced technical architect who can translate BFSI domain needs into scalable cloud data architectures on AWS.
Strong background in enterprise-scale data engineering with expertise in AI/ML workflows, LLM architectures, and data pipeline optimization.
Strategic thinker with proven ability to lead framework development, governance, and mentor junior engineers in complex distributed systems.