Lead Software Engineer - Data Engg. - Databricks / Snowflake

JPMorgan Chase & Co.; Plano (TX); On-site; USD 150,000 - 230,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a Lead Software Engineer in Plano, TX, to design, develop, and maintain scalable data pipelines and governance systems using Databricks, Python, and AWS.

You will lead cross-functional teams, advance data-management strategies, and mentor engineers while embedding responsible AI practices and secure development standards.

Qualifications

• Formal software engineering experience with 5+ years.

Responsibilities

• Execute data-driven software solutions end-to-end, design and troubleshoot complex problems.
• Design and build a control plane for enterprise data pipelines, standardizing definition, scheduling, deployment, governance, and run-time management.
• Develop self-service APIs/SDKs, templates, and configuration-driven onboarding with guardrails and centralized pipeline metadata.
• Design, develop, and maintain scalable data pipelines and processing workflows using Python, PySpark, SQL, Databricks on AWS; model analytics data.
• Ensure data quality, security, lineage, and observability with dashboards, alerts, runbooks, and automated remediation patterns.
• Lead SDLC activities and provide production support to improve stability and reliability.
• Collaborate with stakeholders to shape data management strategy and document data flows and transformation rules.
• Mentor engineers and drive adoption of modern engineering practices and AI-assisted development tools.
• Advise on responsible AI use, data sensitivity, and secure adoption within delivery practices.

Skills

Python

PySpark

Databricks

AWS

SQL

APIs/SDKs

SRE/production support

Observability

CI/CD

AI-assisted development tools

Data governance

Security-by-design

Data quality

Tools

Databricks

Jenkins

Spinnaker

Sonar

Job description

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorgan Chase within the (IAM) Identity and Access Management 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 end-to-end (design, development, troubleshooting), thinking beyond routine approaches to solve complex technical problems.
• Design and build acontrol planefor enterprise data pipelines, standardizing pipeline definition, scheduling, deployment, governance, and run-time management (Databricks today; extensible for future engines).
• Develop self-serviceAPIs/SDKs, templates, and configuration-driven onboarding with consistent guardrails (standards, validation, environment promotion, approvals) and centralized pipeline metadata (ownership, SLAs/SLOs, dependencies, schema/parameter/version tracking).
• Design, develop, and maintain scalabledata pipelinesand processing workflows using Python, PySpark, SQL, Databricks on AWS; develop fact/dimension models for analytics and reporting.
• Ensure data quality, security, lineage, and operational transparency via standardized observability (logs/metrics/traces), dashboards, alerting, runbooks, and automated remediation patterns (retries/backfills, common-failure automation).
• Lead and participate in the full SDLC (requirements, design, build, test, deploy, maintain), acting as SRE/production support for pipeline and platform services to improve stability and reliability.
• Collaborate with stakeholders to shape data management strategy and translate requirements into scalable, compliant solutions; document data flows, logic, and transformation rules for knowledge sharing.
• Mentor engineers and lead communities of practice to drive adoption of modern engineering practices and tools, fostering an inclusive, high-performing culture; utilize firm-approved AI-assisted development tools to accelerate delivery and testing
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Proven experience indata management and ETL/ELTfor large-scale processing, including strong SQL, Python, and PySpark with performance tuning and query optimization.
• Hands-on experience withDatabricks/Sparkand cloud data lake patterns, integrating compute/workflows with AWS services (e.g., S3, ECS, SNS/SQS, Lambda).
• Proven experience buildingplatform services/control planes(or similar orchestration/automation platforms), including API/service design, configuration-driven systems, and versioning/backward compatibility.
• Strong understanding ofdata quality, security-by-design, and lineage/auditability, including IAM/least privilege and secrets management principles.
• Strong production engineering mindset:observability(logs/metrics/traces), monitoring/alerting, incident response, and operational excellence for always-on services.
• Proficiency inCI/CD and release engineering(quality gates, automated testing, safe deployments/rollbacks) using firm-standard tooling (e.g., Jenkins/Jules, Spinnaker, Sonar).
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

Preferred qualifications, capabilities, and skills

• Experience with orchestration/execution frameworks (Databricks Workflows/Jobs, Airflow, Step Functions) and operational patterns such as dependency graphs (DAGs), replays, and backfills.
• Experience with data governance integrations (e.g., Unity Catalog concepts such as cataloging, permissions, and lineage hooks), where applicable.
• Infrastructure-as-Code experience (Terraform/CloudFormation) and developer-platform “golden path” enablement (internal CLIs, templates, paved roads, onboarding automation).
• Experience with FinOps/cost controls for Spark/Databricks workloads (telemetry, quotas, chargeback/showback) and data formats (Parquet, JSON, CSV, Avro, Delta Lake), Knowledge of regulatory reporting and financial data aggregation techniques; Databricks and/or AWS certifications.