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Tier-1 brand, metro location, and a sought-after data engineer title increase competition moderately.
Core data engineering skills are transferable, but regulatory domain and specific tech stack increase specificity.
Multiple mandatory technologies, regulated enterprise experience, and governance requirements raise filtering strictness.
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Build and maintain enterprise-scale data pipelines and data architectures using Apache Spark (Scala/PySpark) within AWS ecosystem.
Design and implement data warehouses and lakes with emphasis on data quality, security, and operational stability, including management of data volumes and velocity.
Collaborate with data scientists to deploy machine learning models, and provide technical leadership or subject matter expertise influencing long term data platform strategy and risk management.
Strong hands-on experience building data pipelines using Apache Spark (Scala & PySpark).
Experience building and operating enterprise-scale data platforms with Infrastructure as Code (IaC) / Terraform.
Proven proficiency with AWS services including S3, AWS Glue, Athena, Step Functions, Lambda, and expertise in Apache Iceberg.
Work Experience Required: Not explicitly mentioned in the JD; Location Requirement: Pune, India.
Experienced in large, regulated enterprise environments with complex data governance and compliance requirements.
Proficient in modern Lakehouse architectures, including tools like dbt, Snowflake, Databricks integration with AWS data lakes.
Ability to lead multi-year technical assignments and guide teams or act as a subject matter expert in data engineering and enterprise data platform delivery.