Data Engineer

Job DescriptionThis role is categorized as hybrid. This means the successful candidate is expected to report to Warren Global Technical Center, Mountain View Technical Center, or Austin Technical Center three times per week, at minimum [or other frequency dictated by the business if more than 3 days].The RoleThis 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.What You’ll DoDesign, 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.Your Skills & Abilities (Required Qualifications)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 DatabricksExperience building and supporting ETL/ELT pipelines in DatabricksExperience developing pipelines using Python, notebooks, DataStage, and scalable data transformation patternsExperience 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.What Can Give You a Competitive Advantage (Preferred Qualifications)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.This job may be eligible for relocation benefits.Compensation: 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 th