at J.P. Morgan
Bulge Bracket Investment BanksPosted 2 months ago
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**Data Engineer III - Python/ Data Lake**: Design and deliver scalable data solutions. Expertise in Python, Spark, AWS data lake, and orchestration tools needed. 3+ years in data engineering required.
- Compensation
- Not specified USD
- City
- New York City
- Country
- United States
Currency: $ (USD)
Full Job Description
Location: New York, NY, United States
Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.
As a Data Engineer III- Python / Data Lake at JPMorganChase within the Consumer and Community Bank - Connected Commerce Technology, you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firms business objectives.
Job responsibilities
- Supports review of controls to ensure sufficient protection of enterprise data
- Advises and makes custom configuration changes in one to two tools to generate a product at the business or customer request
- Updates logical or physical data models based on new use cases
- Frequently uses SQL and understands NoSQL databases and their niche in the marketplace
- Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification on data engineering concepts and 3+ years applied experience
- Experience across the data lifecycle
- Expertise in Python programming language for data engineering tasks (secondary alternative: Java)
- Expertise in cluster computing frameworks such as Spark or Flink
- Experience in building data lakehouse platforms (AWS data lake or Databricks or Hadoop)
- Experience in building DAGs/workflows using scheduling/orchestration tools (Airflow or AWS Step Functions or similar)
- Advanced at SQL (e.g., joins and aggregations)
- Working understanding of NoSQL databases
- Significant experience with statistical data analysis and ability to determine appropriate tools and data patterns to perform analysis
- Experience customizing changes in a tool to generate product
Preferred qualifications, capabilities, and skills
- Proficiency in developing data pipelines using AWS services such Glue, EMR, MSK, Kinesis, etc.
- Experience in using relational data stores (Postgres or similar) and NOSQL data stores (Cassandra or Dynamo or similar)
- Proficiency in IAC (Terraform)
- Knowledge of data serialization formats (e.g., JSON, Avro, Protobuf), big-data storage formats (e.g., Parquet, Iceberg, Hudi), data processing methodologies (batch, micro-batching, stream), and data modeling techniques (Dimensional, Data Vault, Kimball, Inmon)
