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Lead Software Engineer - Python, Databricks, AWS
lead · Technology / Software Development
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. Join our innovative External Regulatory Financial Control (ERFC)/Strategic Data team at JPMorganChase where we leverage cutting-edge technology to drive data-driven decision-making and enhance business performance. We are seeking a talented and motivated Software/Data Engineer to join our team and contribute to our mission of transforming data into actionable insights.
As a Lead Software Engineer at JPMorgan Chase within the External Regulatory Financial Control (ERFC)/Strategic Data team, you will play a crucial role in designing, developing, and maintaining scalable data processing solutions using Databricks, Python, and AWS. You will collaborate with cross-functional teams to deliver high-quality data solutions that support our business objectives.
Job responsibilities
- Execute creative, data-driven software solutions, including design, development, and technical troubleshooting, with the ability to think beyond routine approaches to solve technical problems.
- Design, develop, and maintain scalable data pipelines and processing workflows using Python, PySpark, SQL, and Databricks on AWS, processing and transforming large-scale financial datasets for analytics and reporting.
- Develop fact and dimension data models for reporting and analytics.
- Write secure, high-quality production code, and review and debug code written by others.
- Ensure data quality, consistency, security, and lineage throughout all stages of data processing and transformation, implementing monitoring and alerting mechanisms to maintain pipeline reliability.
- Support data migration and modernization initiatives, transitioning legacy systems to cloud-based data warehouses.
- Lead communities of practice across Software Engineering to promote awareness and adoption of new technologies. Foster a team culture of diversity, opportunity, inclusion, and respect.
- Mentor and guide Associate-level engineers, supporting their technical development and ensuring consistent delivery standards across the team.
- Collaborate with business stakeholders to develop data management strategies, transforming data into insights that drive strategic decisions.
- Document data flows, logic, and transformation rules to maintain transparency and facilitate knowledge sharing.
- Lead and Participate in the full Software Development Life Cycle (SDLC), including requirements gathering, design, development, testing, deployment, and maintenance
- Identify and automate remediation of recurring issues to improve operational stability, acting as both Production Support and SRE function for data pipelines and platform services.
- Utilise AI tools to accelerate development and testing of data pipelines (e.g. GitHub CoPilot, Claude Code).
Required qualifications, capabilities, and skills
Proven experience in data management, ETL/ELT pipeline development, and large-scale data processing.
Proficiency in SQL, Python, and PySpark, with experience in query optimization and performance tuning.
Hands-on experience with data lake platforms (Databricks, Apache Spark, or similar).
Experience with AWS cloud services (S3, ECS, SNS/SQS, Lambda, etc.).
Strong understanding of data quality, security, and lineage best practices.
Experience with cloud-based data warehouse migration and modernization.
Proficient in CI/CD, continuous delivery methods (Jules/Jenkins, Spinnaker, Sonar), the full Software Development Life Cycle, and Agile methodologies.
In-depth knowledge of the financial services industry and their IT systems.
Excellent problem-solving, troubleshooting, and analytical skills with ability to investigate data issues, identify root causes, and implement solutions.
Strong communication and documentation abilities, with the ability to collaborate effectively with business and technical stakeholders.
Preferred qualifications, capabilities, and skills
- Knowledge of regulatory reporting and financial data aggregation techniques.
- Experience with data orchestration tools (Airflow, Step Functions, etc.).
- Databricks or AWS certifications.
- Demonstrated proficiency in cloud-native development (e.g., cloud, artificial intelligence, machine learning).
- Experience with Parquet, JSON, CSV, Avro, Delta Lake file formats.