Data Engineer Job at General Motors (Austin)

Job Description

This role will focus on designing, developing, and supporting Databricks-based pipelines, medallion-layer data products, and enterprise integrations that enable analytics, reporting, and AI use cases. The role also includes building and supporting data movement patterns both into Databricks and between enterprise applications, including solutions that leverage DataStage and related integration technologies. You will partner closely with product owners, architects, data engineers, report and analytics teams, and source-system teams to define trusted data products, improve data quality and reliability, and deliver scalable solutions that support operational and executive decision-making.

Job Responsibility

Design, build, and maintain scalable data pipelines in Databricks to ingest, transform, validate, and publish trusted data products for analytics, reporting, and AI use cases
Develop and support end-to-end ETL/ELT workflows using Databricks notebooks, Python, Spark, and SQL, including orchestration, parameterization, error handling, restartability, and performance optimization
Build and maintain Bronze, Silver, and Gold data products that are reusable, governed, and aligned to business and downstream consumption needs
Build and support integrations both into Databricks and between enterprise applications, including legacy and modern integration patterns such as DataStage-based workflows
Implement pipeline logic for ingestion, standardization, cleansing, enrichment, joins, aggregations, and publishing of curated data assets for downstream use
Partner with product owners, architects, source-system teams, report and analytics teams, and data consumers to translate business needs into well-defined technical solutions and trusted data products
Define and implement data transformations, semantic structures, and curated data assets that improve usability, consistency, downstream performance, and trust in the data
Apply strong data quality, validation, reconciliation, and documentation practices to ensure data products are accurate, discoverable, reliable, and production-ready
Use GitHub-based development practices for version control, code review, collaboration, and promotion of pipeline changes across environments
Support secure and compliant data delivery by implementing access controls, permissions, and governance requirements in alignment with GM policies
Monitor, troubleshoot, and improve pipeline health, runtime performance, cost efficiency, and operational stability across production data assets and integrations
Help modernize legacy integrations and reporting patterns by standardizing and migrating solutions onto the enterprise data platform
Contribute to team standards, reusable patterns, and best practices for notebooks, Python development, GitHub workflows, data engineering, integration design, data quality, and operational support

Requirements

Bachelor's degree in Computer Science, Information Systems, Data Engineering, Data Science, Engineering, or a related field
or equivalent experience
5+ years of experience as a data engineer, ETL developer, or integration engineer building production-grade data pipelines and data products
Hands-on experience with Databricks for data engineering and analytics enablement, including: Strong SQL skills in Databricks
Experience building and supporting ETL/ELT pipelines in Databricks
Experience developing pipelines using Python, notebooks, DataStage, and scalable data transformation patterns
Experience with workflow orchestration, dependency management, scheduling, monitoring, and operational support of production pipelines
Proven experience designing and implementing dimensional, layered, or medallion-style data models for analytics and operational use cases
Strong knowledge of data warehousing and ETL/ELT concepts, including how upstream design impacts downstream performance, usability, and trust in data products
Experience integrating data from enterprise applications, especially operational platforms such as ServiceNow
Familiarity with DataStage and application-to-application integration patterns
Experience using GitHub for source control, branching, pull requests, collaboration, and release management of data engineering assets
Demonstrated ability to implement data quality, metadata, documentation, and governance practices in production data environments
Strong collaboration skills and a track record of working effectively in cross-functional teams (data engineers, architects, product owners, business partners, and report and analytics teams)
Strong problem-solving, communication, and ownership skills, with the ability to operate effectively in a fast-moving environment

Nice to have

Experience supporting analytics, dashboards, or executive reporting use cases
Experience working with ServiceNow data, including ITSM, CMDB, HRSD, or related operational domains
Experience with secure data delivery, access controls, and enterprise governance standards
Experience with production support, observability, and operational reporting for data platforms
Familiarity with data dictionaries, lineage, and data product documentation or publishing practices
Familiarity with cloud data platform patterns, particularly Databricks Lakehouse environments
Experience working in Agile or product-centric environments with iterative delivery and continuous feedback

What we offer

health and wellbeing benefit programs
medical
dental
vision
Health Savings Account
Flexible Spending Accounts
retirement savings plan
sickness and accident benefits
life insurance
paid vacation & holidays
tuition assistance programs
employee assistance program
GM vehicle discounts
relocation benefits

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