Aditya Das

Data Engineer | SQL | Informatica PowerCenter | ETL Pipelines | Data Warehousing | Banking Domain | Tableau | Power BI

Bengaluru, Karnataka, India

About

Data Engineer with 3.6+ years of experience building and supporting enterprise #ETL pipelines, SQL-based data solutions, workflow orchestration, and data warehousing systems for global banking platforms. Experienced in Informatica PowerCenter, SQL optimization, Autosys scheduling, Tableau, Power BI, and production support. Skilled in data validation, reconciliation, root cause analysis, incident management, and cross-functional collaboration. Open to opportunities in Data Engineering, ETL Development, SQL Development, Data Operations, and Analytics.

Experience

  • Capgemini (3 yrs 6 mos)
    • Data Specialist
      2024 - Present · 2 yrs 6 mos

      Project 2: Production System Operations and Data Pipeline Support. • Monitored Autosys-scheduled data pipelines, workflows and production batch jobs. • Investigated job failures, validated upstream/downstream data and ensured data pipeline continuity. • Used ServiceNow for managing tickets, incidents and change requests. • Worked with Informatica mappings and ETL workflows to support data engineering teams. • Documented incidents, fixes and analysis notes for audit and compliance. • Strengthened knowledge of workflow orchestration, dependency management and Jump Server operations.

    • Data Quality Analyst
      2023 - 2024 · 1 yr

      Project 1: EDP - Golden Source Data Platform. • Monitored, validated, and executed daily ETL data loads across MUFG's Golden Source System. • Performed checksum validations, reconciliation and variance analysis to ensure high data integrity. • Identified anomalies using root-cause analysis and supported the reduction of recurring data issues. • Updated SOPs, flow diagrams, and runbooks to streamline operational workflows. • Used Tableau to extract datasets from MUFG's warehouse for validation and reporting. • Maintained adjustment trackers to ensure the transparency of data corrections. • Utilized advanced Excel (pivot tables, lookups, formulas) for analysis and reporting. • Gained hands-on exposure to ETL processes, source–target mappings and data modeling.