Data-ETL Engineering Lead-Vice President

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

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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

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## Job Family:

Applications Development

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## Time Type:

Full time

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## Most Relevant Skills

Please see the requirements listed above.

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## Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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