JPMorgan Sr Lead Software Engineer - Data & Analytics Platform/Engineering 

United States, New Jersey, Jersey City 

386367223

Job responsibilities

• Implements and delivers engineering solutions/tools and data products using cloud and on-premises data, distributed computing and emerging technologies.
• Develops secure and high-quality production code, and reviews and debugs code written by others.
• Contributes through the full software development lifecycle including architecture, proofs of concept, prototyping, development, rollout and support.
• Prioritize solving customer requests and issue reports, participate in support coverage
• Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
• Adds to the team culture of diversity, equity, inclusion, and respect

Required qualifications, capabilities, and skills

• Formal training or certification on Computer Science / Engineering concepts and 5+ years hands on professional experience in building complex software systems in both private and public cloud environments (AWS)
• Degree in Computer Science, Computer Engineering, Mathematics, or a related technical field
• Advanced knowledge of software applications and technical processes with considerable in-depth knowledge of application, data, cloud and infrastructure architecture disciplines.
• Advanced skill in Java and Python.
• Strong hands-on experience with PostgreSQL, AWS RedShift, Athena, Glue, Kafka and NoSQL (Cassandra, MongoDB).
• Experience with fundamental DevOps practices including CI/CD.
• Ability to tackle design and functionality problems independently. A self-starter and thrive in a fast-paced, agile setting.
• Have clear and effective verbal and written communication skills and ability to communicate seamlessly across tech and data scientist teams

• Excellent problem-solving and analytical skills
• Containers and cloud proficiency including Docker, Kubernetes, AWS
• Hands on experience in building ETL/Data Pipeline and data lake platforms (e.g. Databricks, Spark/Hadoop, and Snowflake)
• Knowledge of workflow orchestration tools (e.g. Apache Airflow), integration technologies (e.g. GraphQL, REST)
• Experience building, deploying Machine Learning models, and the ML Lifecycle