Lead Software Engineer Databricks, Spark, AWS

Plano, TX - USA

Monthly Salary: Not Disclosed

Posted on: 19 hours ago

Vacancies: 1 Vacancy

Job Summary

Description

This is your chance to change the path of your career and guide multiple teams to success at one of the worlds leading financial institutions.

As a Lead Software Engineer at JPMorgan Chase within Corporate Sector Chief Technology Office youare an integral part of an agile team that works to enhance build and deliver trusted market-leading technology products in a secure stable and scalable way. As a core technical contributor you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firms business objectives.

Job Responsibilities:

Lead architecture and delivery of high-throughput low-latency data pipelines using Databricks and Apache Spark (Core SQL Structured Streaming).
Establish lakehouse patterns with Delta Lake (ACID transactions schema evolution time travel Z-ordering compaction) and ensure performance at scale.
Own Databricks cluster strategy and setup: runtime selection autoscaling driver/executor sizing Spark configs init scripts cluster policies pools and instance profiles.
Orchestrate jobs with Databricks Workflows; integrate with AWS eventing and orchestration as needed.
Design secure data ingestion and transformation frameworks leveraging AWS services:
- S3 for data lake storage and lifecycle management
- Glue for catalog/metadata and ETL jobs
- IAM and Secrets Manager for role-based access and credential management
- CloudWatch for logging metrics and alerting
- Lambda for serverless utilities
- Kinesis and/or Kafka/MSK for streaming ingestion
Enforce data quality lineage and governance using Unity Catalog and/or Glue Catalog; embed expectations and validation into pipelines.
Drive Spark performance engineering: partitioning strategies file sizing AQE broadcast joins shuffle tuning caching spill/memory control and job right-sizing to optimize cost.
Build reusable libraries frameworks and APIs in Python and/or Java; oversee unit integration and data validation testing.
Implement CI/CD for data projects (Git-based workflows) Terraform Infrastructure deployments environment promotion and automated deployments; champion engineering standards and code reviews.
Partner with platform security and networking teams to enforce encryption network controls and least-privilege access; ensure compliance with organizational policies.
Lead incident response and root-cause analysis; establish SLAs observability and runbooks; drive continuous improvement in reliability and cost efficiency.

Required qualifications capabilities and skills:

Formal training or certification on software engineering concepts and 5 years applied experience.
10 years of professional software/data engineering experience including substantial production work with Spark on Databricks or EMR.
Strong proficiency in Python and/or Java for data processing platform tooling and automation.
Hands-on Databricks expertise (Delta Lake Unity Catalog Workflows Repos/notebooks SQL Warehouses).
Solid AWS experience: S3 IAM Glue CloudWatch Kinesis / MSK DynamoDB
Proven track record architecting and operating ETL/ELT pipelines (batch and streaming) with schema design/evolution SLAs and reliability engineering.
Deep skills in Spark performance tuning and Databricks cluster setup/optimization.
Strong SQL and analytics data modeling (dimensional/star schema; lakehouse best practices).
Security-first mindset: roles/instance profiles secret management encryption-at-rest/in-transit and network controls.
Demonstrated leadership in code quality reviews testing strategy CI/CD and technical mentorship; excellent communication with stakeholders.

Preferred qualifications capabilities and skills:

Experience with Delta Live Tables and advanced governance (catalogs grants auditing) in Databricks.
AWS networking knowledge (VPC subnets routing security groups) and data egress controls.
Experience with Terraform for Infra deployments
Cost optimization experience: autoscaling strategies spot vs on-demand auto-termination storage layouts and compaction.
Familiarity with Kafka/MSK or Kinesis Data Streams/Firehose for real-time ingestion.
CI/CD and automation tooling for data (Git workflows artifact management) and testing frameworks (pytest JUnit).
Observability for data systems (freshness/completeness metrics lineage SLAs alerting).
Experience in financial services or other regulated industries.

Required Experience:

DescriptionThis is your chance to change the path of your career and guide multiple teams to success at one of the worlds leading financial institutions.As a Lead Software Engineer at JPMorgan Chase within Corporate Sector Chief Technology Office youare an integral part of an agile team that works t...

Description

This is your chance to change the path of your career and guide multiple teams to success at one of the worlds leading financial institutions.

Job Responsibilities:

Lead architecture and delivery of high-throughput low-latency data pipelines using Databricks and Apache Spark (Core SQL Structured Streaming).
Establish lakehouse patterns with Delta Lake (ACID transactions schema evolution time travel Z-ordering compaction) and ensure performance at scale.
Own Databricks cluster strategy and setup: runtime selection autoscaling driver/executor sizing Spark configs init scripts cluster policies pools and instance profiles.
Orchestrate jobs with Databricks Workflows; integrate with AWS eventing and orchestration as needed.
Design secure data ingestion and transformation frameworks leveraging AWS services:
- S3 for data lake storage and lifecycle management
- Glue for catalog/metadata and ETL jobs
- IAM and Secrets Manager for role-based access and credential management
- CloudWatch for logging metrics and alerting
- Lambda for serverless utilities
- Kinesis and/or Kafka/MSK for streaming ingestion
Enforce data quality lineage and governance using Unity Catalog and/or Glue Catalog; embed expectations and validation into pipelines.
Drive Spark performance engineering: partitioning strategies file sizing AQE broadcast joins shuffle tuning caching spill/memory control and job right-sizing to optimize cost.
Build reusable libraries frameworks and APIs in Python and/or Java; oversee unit integration and data validation testing.
Implement CI/CD for data projects (Git-based workflows) Terraform Infrastructure deployments environment promotion and automated deployments; champion engineering standards and code reviews.
Partner with platform security and networking teams to enforce encryption network controls and least-privilege access; ensure compliance with organizational policies.
Lead incident response and root-cause analysis; establish SLAs observability and runbooks; drive continuous improvement in reliability and cost efficiency.

Required qualifications capabilities and skills:

Formal training or certification on software engineering concepts and 5 years applied experience.
10 years of professional software/data engineering experience including substantial production work with Spark on Databricks or EMR.
Strong proficiency in Python and/or Java for data processing platform tooling and automation.
Hands-on Databricks expertise (Delta Lake Unity Catalog Workflows Repos/notebooks SQL Warehouses).
Solid AWS experience: S3 IAM Glue CloudWatch Kinesis / MSK DynamoDB
Proven track record architecting and operating ETL/ELT pipelines (batch and streaming) with schema design/evolution SLAs and reliability engineering.
Deep skills in Spark performance tuning and Databricks cluster setup/optimization.
Strong SQL and analytics data modeling (dimensional/star schema; lakehouse best practices).
Security-first mindset: roles/instance profiles secret management encryption-at-rest/in-transit and network controls.
Demonstrated leadership in code quality reviews testing strategy CI/CD and technical mentorship; excellent communication with stakeholders.

Preferred qualifications capabilities and skills:

Experience with Delta Live Tables and advanced governance (catalogs grants auditing) in Databricks.
AWS networking knowledge (VPC subnets routing security groups) and data egress controls.
Experience with Terraform for Infra deployments
Cost optimization experience: autoscaling strategies spot vs on-demand auto-termination storage layouts and compaction.
Familiarity with Kafka/MSK or Kinesis Data Streams/Firehose for real-time ingestion.
CI/CD and automation tooling for data (Git workflows artifact management) and testing frameworks (pytest JUnit).
Observability for data systems (freshness/completeness metrics lineage SLAs alerting).
Experience in financial services or other regulated industries.

Required Experience:

Key Skills

Spring
.NET
C/C++
Go
React
OOP
C#
Data Structures
JavaScript
Software Development
Java
Distributed Systems

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About Company

JPMorganChase

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans ov ... View more

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