Data-ETL Engineering Lead-Vice President

Data-ETL Engineering Lead-Vice President Job Req Id: 26994927 Location(s): Pune, Maharashtra, India Job Type: Hybrid Posted: Sep. 28, 2026 Discover your future at Citi Working at Citi is far more than just a job. A career with us means joining a team of approximately 219,000 dedicated people from around the globe. At Citi, you’ll have the opportunity to grow your career, give back to your community and make a real impact. Job Overview The Data & ETL Engineering Lead (C13) is a senior technical leadership role responsible for architecting, designing, and delivering enterprise-scale data integration, ETL/ELT pipelines, and data warehousing solutions. This role requires an expert data engineer with deep hands-on proficiency in Ab Initio , modern Python-based data engineering , and relational database engines ( Oracle DB ). As a C13 Data Lead, you will oversee end-to-end data delivery across the Software Development Life Cycle (SDLC), collaborate closely with cross-functional business and technical stakeholders, define data architecture and modeling standards, and implement automated CI/CD deployment pipelines for high-throughput batch and real-time processing systems. Key Responsibilities 1. Technical Leadership & Data Architecture ETL & Pipeline Architecture: Lead the architecture, design, and implementation of robust, high-volume batch and real-time ETL/ELT pipelines using Ab Initio and Python . Data Warehousing Design: Define and implement dimensional data models (Star Schema, Snowflake Schema, Slowly Changing Dimensions - SCD Type 1/2/3/4/6, Conformed Dimensions, Fact Tables) supporting large-scale enterprise reporting and analytics. Data Governance & Quality: Enforce enterprise data governance standards, data lineage, metadata management, data dictionary maintenance, and automated data validation/reconciliation frameworks. 2. Database Engineering & Performance Optimization Oracle Database Development: Lead database design, complex SQL authoring, and advanced PL/SQL programming (Stored Procedures, Packages, Triggers, Table Functions). Performance Tuning: Perform comprehensive performance tuning of large-scale ETL graphs, Python jobs, and Oracle queries via execution plans, indexing strategies, table partitioning, parallel execution, and optimizer hints. Volume Management: Architect solutions capable of processing multi-terabyte datasets within stringent SLA time windows. 3. Stakeholder Management & Collaboration Cross-Functional Partnership: Act as the primary technical liaison between business stakeholders, data product owners, quantitative analysts, reporting teams, and enterprise infrastructure partners. Requirements Translation: Translate complex business rules and regulatory requirements into detailed technical specifications, source-to-target mappings (STTM), and data flow architectures. Agile & Program Delivery: Partner with Scrum Masters and Project Managers to plan sprint roadmaps, estimate technical effort, mitigate data pipeline risks, and manage dependency handoffs. 4. CI/CD & DevOps Automation DevOps for Data Pipelines: Build and standardize automated CI/CD pipelines for packaging, testing, and deploying Ab Initio code/graphs, Python scripts, and Oracle DDL/DML migrations (e.g., using Jenkins, Harness, Tekton, GitLab CI, Liquibase). Version Control & Release Management: Manage code repositories, branching workflows, and configuration management across environments (Dev, SIT, UAT, Prod). Operational Monitoring & Production Resilience: Establish monitoring and alerting systems (e.g., Autosys, Control-M, Grafana, Splunk, Loki), lead Root Cause Analysis (RCA) for critical batch failures, and drive operational stability. 5. Team Mentorship & Engineering Standards Team Leadership: Mentor and guide mid-level and junior ETL developers, data analysts, and database engineers. Standardization: Establish code review checklists, design patterns, reusable ETL modules/subgraphs, and automated unit/regression testing standards across data engineering teams. Technical Skills & Competencies ETL & Data Integration Deep hands-on expertise in Ab Initio (Co>Operating System, GDE, Enterprise Meta>Environment (EME), Continuous Flows, Plan>It, Express>It, Component Development, Subgraphs, Partitioning/De-partitioning) Strong experience building custom data extractors, loaders, and transformers Python Data Engineering Advanced Python 3.x for data processing and pipeline scripting Proficiency with libraries such as Pandas, NumPy, PyArrow, SQLAlchemy, PySpark, Polars Writing clean, object-oriented, testable Python code with unit test coverage (pytest/unittest) Data Warehousing & Modeling Comprehensive understanding of Data Warehousing & Data Lakehouse concepts (Inmon vs. Kimball methodologies) Dimensional modeling (Star / Snowflake schemas, Factless Facts, Aggregate tables, SCD Types) Data lineage, Source-to-Target Mappings (STTM), metadata governance, and data profiling Database & SQL Advanced Oracle 19c+ & PL/SQL programming (Complex joins, window functions, analytical functions, CTEs) Deep knowledge of Oracle optimizer, query execution plans, indexes (B-tree, Bitmap), partitioning/sub-partitioning strategies, and bulk operations (FORALL, BULK COLLECT) CI/CD & Infrastructure Experience in CI/CD pipeline authoring ( Jenkins, Harness, Tekton, GitHub Actions, GitLab CI ) Database change management tools (e.g., Liquibase, Flyway ) Linux/Unix shell scripting ( Bash/Ksh ), job scheduling tools ( Autosys, Control-M, Airflow ) Version control with Git / Bitbucket Testing & Quality Automated data testing, data reconciliation, boundary testing, and regression suites Code quality tools and security scanners ( SonarQube, Checkmarx, Snyk ) Experience & Leadership Profile Total Experience: 10+ years of professional experience in data engineering, data warehousing, and ETL development, with at least 3+ years leading technical teams or complex data engineering initiatives. Education: Bachelor’s or Master’s degree in Computer Science, Information Systems, Software Engineering, Data Analytics, or equivalent quantitative discipline. Domain Knowledge: Prior experience in banking, financial services (e.g., Risk, Regulatory Reporting, Capital Markets, Retail Banking, Wealth Management), or large enterprise data systems is highly preferred. Communication & Influence: Proven ability to communicate effectively with business stakeholders, summarize complex technical data architectures, and lead discussions with senior leadership. Job Family Group: Technology Job Family: Applications Development Time Type: Full time Most Relevant Skills Please see the requirements listed above. Other Relevant Skills For complementary skills, please see above and/or contact the recruiter. 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