Play a vital role in shaping the future of an iconic company and make a direct impact in a dynamic environment designed for top achievers.
As a Lead Data Engineer at JPMorgan Chase within the Contact Central Team ,you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities:
- Lead the Team to deliver data pipeline for Ingestion, Transformation, Mastering of Contact data including Client relationship build
- Constructs and optimizes data pipelines for ETL/ELT operations, ensuring efficient data processing and transformation to meet business needs.
- Designs and delivers trusted data collection, storage, access and data platform solutions in a secure, stable, and scalable way.
- Provides recommendations and insight on data management, governance procedures, and intricacies applicable to the acquisition, maintenance, validation, and utilization of data.
- Develops and implements data federation strategies using APIs, Data share, messaging services
- Utilizes ANSI SQL to enable efficient data aggregation and manipulation across multiple data sources, enhancing analytical capabilities and decision-making processes.
- Designs and maintains robust software solutions for authorization and key management, ensuring secure connectivity to both private and public cloud environments.
- Builds and manages container images for custom services and applications, as well as third-party open-source applications, to support scalable and reliable deployment in cloud environments.
- Evaluates and reports on access control processes to determine effectiveness of data asset security with minimal supervision.
- Adds to team culture of diversity, equity, inclusion, and respect.
- Develop solution in well architected framework addressing scalability, resiliency, cost, operational excellence.
Required qualifications, capabilities, and skills
Formal training or certification on data and software engineering concepts and 5+ years applied experience Strong experience with both relational ( Aurora) and NoSQL databases.
Experience and proficiency across the data lifecycle.
Experience in building and deploying applications within containerized environments utilizing tools such as Kubernetes , Docker and ECS
Strong proficiency in programming languages such as Python, JAVA and SQL.
In-depth knowledge of OLTP , data warehousing solutions and ETL processes.
Ability to work collaboratively with cross-functional teams, including data scientists, analysts, and business stakeholders.
Excellent communication skills, with the ability to convey complex technical concepts to non-technical audiences.
Experience with version control systems like Git and CI/CD pipelines for data engineering workflows.
Solid understanding of the Parquet, ORC and AVRO file formats.
Experience in ETL tools ( Glue , data bricks)
Preferred qualifications, capabilities, and skills
Proficiency in Big Data technologies, with a strong focus on Performance Optimization using best practices.
Solid understanding of open table formats particularly iceberg.
Experience in building, maintaining and optimizing iceberg tables.
Proficiency in building and maintaining efficient container images,
Experience with orchestration tools such as Airflow, AWS step , Event bridge