Join us as we embark on a journey of collaboration and innovation, where your unique skills and talents will be valued and celebrated. Together we will create a brighter future and make a meaningful difference.
As a Lead Data Engineer (VP) at JPMorganChase within Cloud Foundational Services, you are an integral part of an agile team that works to enhance, build, and deliver advanced data engineering, analytics, and applied machine learning solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for designing and maintaining critical data pipelines, reporting datasets, and AI-driven architectures across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Lead the design, development, and optimization of complex SQL and PL/SQL solutions, with a strong focus on Oracle Database environments.
- Generate and maintain reusable reporting datasets to support multiple dashboards, reports, and business use cases, ensuring data quality, completeness, and consistency.
- Deliver data collection, storage, access, analytics, and machine learning platform solutions in a secure, stable, and scalable way.
- Implement and oversee database backup, recovery, and archiving strategies, ensuring data integrity and security.
- Collaborate closely with data analysts, product managers, and business stakeholders to gather requirements and translate them into efficient database views and curated data layers for reporting and API consumption.
- Evaluate and report on access control processes to determine effectiveness of data asset security with minimal supervision.
- Design, implement, and maintain materialized views, indexing, and schema changes for performance optimization and data aggregation.
- Build and maintain clear KPI, metric, and dimension logic with consistent business meaning, simplifying and standardizing reporting logic for maintainability and reuse.
- Apply advanced data validation, reconciliation, and root-cause analysis to ensure reporting accuracy and trust.
- Develop and deploy ETL/ELT workflows using tools such as Pentaho Data Integration (Kettle) and modern data transformation platforms.
- Apply machine learning, generative AI (GenAI), Retrieval-Augmented Generation (RAG), and vector embeddings for advanced search, analytics, and insight-led reporting.
- Add to team culture of diversity, opportunity, inclusion, and respect.
Required qualifications, capabilities, and skills
- Extensive hands-on experience with SQL and PL/SQL, ideally in Oracle Database environments.
- Proven ability to write, debug, and optimize complex SQL queries for large-scale, high-performance environments.
- Experience designing, implementing, and maintaining materialized views, packages, procedures, functions, and triggers in Oracle.
- Strong skills in database performance tuning, query optimization, and troubleshooting.
- Experience collaborating with business stakeholders to translate requirements into efficient database solutions.
- Experience and proficiency across the data lifecycle, including data collection, storage, access, and analytics.
- Experience with database backup, recovery, and archiving strategy.
- Strong focus on data quality, completeness, and consistency for reporting purposes.
- Ability to define, document, and maintain clear KPI, metric, and dimension logic.
- Strong analytical skills, including the ability to identify trends, anomalies, inconsistencies, and opportunities within data.
- Experience in data validation, reconciliation, and root-cause analysis.
**Preferred qualifications, capabilities, and skills **
- Familiarity with Pentaho Data Integration (Kettle) or similar ETL/ELT tools.
- Experience working with BI/reporting platforms and understanding how they consume curated datasets.
- Strong capability in Excel for validation, reconciliation, and ad hoc analysis.
- Familiarity with version control and structured development practices.
- Exposure to modern data transformation or analytical tools.
- Experience with Python or similar analytical tooling.
- Working experience with NoSQL databases.
- Familiarity with Generative AI (GenAI) concepts, Retrieval-Augmented Generation (RAG), and vector embeddings in databases.
- Proficient knowledge of linear algebra, statistics, and geometrical algorithms.
- Experience with software development best practices in enterprise environments.
- Excellent communication skills for cross-functional teamwork and documentation of database solutions.