Data Engineer
- 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.
- 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.
- 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.
- The expected base compensation for this role is: $138,700 - $206,950. Actual base compensation within the identified range will vary based on factors relevant to the position.
- Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
- Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include 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 and more.
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