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 Responsibilities1. 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.
- 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.
- 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.
- 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.
- 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
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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.
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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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Skills Required
- 10+ years of professional experience in data engineering, data warehousing, and ETL development
- At least 3 years leading technical teams or complex data engineering initiatives
- Deep hands-on expertise with Ab Initio
- Advanced Python 3.x for data processing and pipeline scripting
- Advanced Oracle Database 19c+ and PL/SQL programming
- Experience with dimensional data modeling and data warehousing or lakehouse concepts
- Experience with CI/CD pipeline authoring and database change management tools
- Experience with Linux or Unix shell scripting and job scheduling tools
- Experience with automated data testing, reconciliation, boundary testing, and regression suites
- Bachelor's or Master's degree in Computer Science, Information Systems, Software Engineering, Data Analytics, or an equivalent quantitative discipline
- Prior experience in banking, financial services, or large enterprise data systems
- Strong communication and stakeholder influence skills
Citi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Citi and has not been reviewed or approved by Citi.
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Healthcare Strength — Benefits coverage is positioned as comprehensive, including health, dental, and vision insurance plus on-site clinics, prescription drug support, and disability coverage. Family-building support such as fertility assistance is described as a notable differentiator within the overall package.
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Retirement Support — Retirement benefits are framed as strong, highlighted by a 401(k) with matching and additional plan options like a Roth 401(k). Financial support is reinforced through discounts and broader financial guidance resources tied to the benefits ecosystem.
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Wellbeing & Lifestyle Benefits — Wellbeing support extends beyond insurance through programs like an Employee Assistance Program, counseling/legal resources, and gym or wellness reimbursement. These offerings increase the perceived total rewards value even when cash compensation sentiment varies by role.
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