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Job description
Lead Data Engineer – Databricks, Python, PySpark & AWS Job Summary We are seeking a Senior Data Engineer / SME to join the SSIM Business Architecture vertical in a hands-on Individual Contributor (IC) role. The role requires strong expertise in Databricks, Python, PySpark, AWS, and SQL to design, build, and support scalable, production-grade data pipelines and cloud data solutions. Snowflake and enterprise data integration experience are desired. The successful candidate will demonstrate strong technical ownership, problem-solving skills, and engineering best practices across the data ecosystem. Role location : Bangalore
Business Architecture: The role sits within the SSIM Business Architecture vertical and requires close collaboration with Architecture, Security, engineering, platform, and business technology stakeholders.
Hybrid working: The role requires 4 days per week working from the office, in line with the applicable workplace expectations.
Working hours: The primary working window will be 12:00 PM to 9:00 PM India time, providing alignment with US stakeholders and teams.
Flexibility: Given the enterprise and production nature of the role, the individual must be flexible to work outside standard hours and, where required, over weekends to support critical deliveries, production issues, releases,DR or other business needs.
Key Responsibilities
Design, develop, and maintain scalable ETL/ELT, data ingestion, and transformation pipelines.
Build and optimize data processing solutions using Python and PySpark.
Develop and support production-grade workloads and pipelines on Databricks.
Design and implement cloud-native data engineering solutions on AWS.
Work with large-scale structured and unstructured datasets.
Optimize Spark and Databricks workloads for performance, scalability, reliability, and cost efficiency.
Develop reusable data engineering components, frameworks, and engineering patterns.
Implement data quality, validation, reconciliation, and monitoring controls.
Troubleshoot complex production issues, perform root cause analysis, and implement sustainable fixes.
Support CI/CD, testing, deployment, monitoring, and operational reliability.
Act as a Data Engineering SME, providing technical guidance, design reviews, and code reviews.
Collaborate with architecture, application, cloud, platform, and business teams to deliver enterprise data solutions.
Drive automation, standardization, and continuous improvement across the data engineering landscape.
Required Skills
Strong hands-on experience in Data Engineering.
Strong hands-on experience with Databricks.
Advanced programming skills in Python and PySpark.
Strong experience with AWS cloud-based data engineering.
Strong proficiency in SQL.
Experience designing and supporting enterprise ETL/ELT and data pipelines.
Strong understanding of Apache Spark and distributed data processing.
Experience with data lake and lakehouse architecture.
Strong understanding of data modeling, data integration, and data quality principles.
Experience with CI/CD and modern engineering practices.
Experience supporting business-critical production data platforms.
Strong troubleshooting, debugging, and root cause analysis skills.
Strong communication and stakeholder-management capabilities.
Desired Skills
Experience with Snowflake data engineering and integration.
Knowledge of Snowflake databases, schemas, warehouses, security, and performance optimization.
Experience integrating Databricks/AWS data pipelines with Snowflake.
Experience with GoldenGate, AWS DMS, or similar data replication technologies.
Knowledge of data governance, lineage, metadata management, and auditing.
Experience with data quality and reconciliation frameworks.
Exposure to enterprise monitoring and observability solutions.
Experience working in financial services or other regulated environments.
Experience Requirements
10+ years of overall experience in software engineering, data engineering, or related technology roles.
8+ years of hands-on data engineering & Devops experience.
7+ years of relevant experience across Databricks, PySpark/Spark, and cloud-based data engineering preferred.
Proven experience designing, developing, and supporting enterprise-scale production data pipelines and platforms.
Demonstrated ability to operate as a hands-on technical SME in complex enterprise environments.
Education Requirements
Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related technical discipline.
Master’s degree in a relevant discipline is desirable.
Technology Stack Core / Mandatory: Databricks | Python | PySpark | AWS | SQL | Apache Spark | ETL/ELT | Data Pipelines | Data Lake/Lakehouse Desired: Snowflake | GoldenGate | AWS DMS | Data Governance | Data Lineage | Data Quality | Observability
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