Embrace this pivotal role as an essential member of a high performing team dedicated to reaching new heights in data engineering. Your contributions will be instrumental in shaping the future of one of the world’s largest and most influential companies.

As a Lead Data Engineer at JPMorgan Chase within the Consumer & Community Banking, Cards Technology Team, you are an integral part of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics in a secure, stable, and scalable way. Leverage your deep technical expertise and problem solving capabilities to drive significant business impact and tackle a diverse array of challenges that span multiple data pipelines, data architectures, and other data consumers.

Job responsibilities: 

Provides recommendations and insight on data management, governance procedures, and intricacies applicable to the acquisition, maintenance, validation, quality, anomaly detection and utilization of data​​ Designs and delivers trusted data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way Defines database back-up, recovery, and archiving strategy  Generates advanced data models for one or more teams using firmwide tooling, linear algebra, statistics, and geometrical algorithms Approves data analysis tools and processes​ Creates functional and technical documentation supporting best practices​​ Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture. Design & develop data pipelines end to end using Spark SQL, Java and AWS Services. Utilize programming languages like Java, Python, NoSQL databases, SQL, Container Orchestration services including Kubernetes, and a variety of AWS tools and services. Contributes to software engineering communities of practice and events that explore new and emerging technologies Evaluates and reports on access control processes to determine effectiveness of data asset security​ Adds to team culture of diversity, equity, inclusion, and respect

 Required qualifications, capabilities, and skills:

Formal training or certification on software engineering concepts and 5+ years of applied experience. Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying language. Hands-on practical experience in developing spark-based Frameworks for end-to-end ETL, ELT & reporting solutions using key components like Spark SQL & Spark Streaming.  Proficient in coding in one or more Coding languages - Java, Python Experience with Relational and No SQL databases, Cloud implementation experience with AWS including: AWS Data Services: Proficiency in Lake formation, Glue ETL (or) EMR, S3, Glue Catalog, Athena, Kinesis (or) MSK, Airflow (or) Lambda + Step Functions + Event Bridge Data De/Serialization: Expertise in at least 2 of the formats: Parquet, Iceberg, AVRO, JSON-LD AWS Data Security: Good Understanding of security concepts such as: Lake formation, IAM, Service roles, Encryption, KMS, Secrets Manager Proficiency in automation and continuous delivery methods.  Solid understanding of agile methodologies such as CI/CD, Applicant Resiliency, and Security

Preferred qualifications, capabilities, and skills: 

Snowflake knowledge or experience preferred  In-depth knowledge of the financial services industry and their IT systems Worked with building Data lake, built Data platforms, built Data frameworks, Built/Design of Data as a Service AP