Overview

Senior Data Engineer / Data Engineering Lead

Responsibilities

• Lead the architecture and development of end-to-end analytics platforms.
• Develop and maintain BI, analytics, and reporting data pipelines.
• Set up AWS Glue for file ingestion and data bucket management.
• Partner with client teams to deploy Databricks workspaces with secure governance.
• Refactor existing SAS scripts into Spark jobs using Databricks Notebooks.
• Build PySpark / Spark SQL ELT jobs using Delta Tables for ACID compliance.
• Orchestrate PySpark transformations using Databricks Workflows.
• Develop and manage monthly batch-processing analytics data pipelines.
• Lead BI development teams supporting credit risk and lending transformation projects.
• Analyze data lineage and implement data accuracy and validation testing.
• Design and develop dashboards based on stakeholder requirements.
• Test and deploy dashboards with row-level data security.
• Transition decision-making analytics dashboards into production.
• Collaborate with global stakeholders to understand business requirements and deliver scalable data solutions.
• Drive modernization, automation, data quality, and production-readiness initiatives.

Qualifications

• Location: Chandigarh
• Work Model: Work from office
• Domain: Banking / Financial Services / Credit Risk | +91-7376738923,7888577467
• Role Type: Full-Time
• About the Role
• We are seeking an experienced Data Engineer with 10+ years of expertise in building and modernizing data assets for banking portfolio optimization.
• The role offers an opportunity to lead teams in modernizing BI, AI, and ML data pipelines, automate manual processes, and build audit-ready production data platforms. The successful candidate will work on impactful projects across wholesale lending, retail lending, and credit risk, collaborating with global stakeholders and driving data transformation initiatives.
• 10+ years of experience in Data Engineering / Data Analytics / BI.
• Strong experience with Python programming.
• Hands-on expertise in PySpark.
• Advanced SQL skills.
• Strong hands-on experience with Databricks.
• Strong experience with Tableau.
• AWS Certified Data Engineer Associate certification.
• Robust understanding of data engineering practices and production data environments.
• SAS programming experience.
• Strong knowledge of Spark SQL.
• Knowledge of data governance, lineage, and data quality frameworks.
• Advanced prompt engineering experience.
• Exposure to AI/ML data pipelines and AI-driven analytics.
• Wholesale Lending
• Retail Lending
• Credit Risk
• Banking Portfolio Optimization
• Financial Services Analytics
• BI & Decision-Making Analytics

Benefits

• Opportunity to work on high-impact banking and financial services transformation projects.
• Exposure to modern AWS, Databricks, Spark, AI/ML, and BI technologies.
• Opportunity to lead data engineering and BI transformation initiatives.
• Collaboration with global stakeholders and client teams.
• Health Insurance
• Paid Time Off
• Learning & Development Opportunities