Senior Software Engineer

Job Description This role is categorized as hybrid. This means the successful candidate is expected to report to Austin Technical Center three times per week, at minimum [or other frequency dictated by the business if more than 3 days].

The Role The GM Energy Data Engineering team is on the lookout for a talented Data Engineer who is a highly skilled and hands-on Cloud Data Warehouse engineer with experience in IoT data integration, Databricks, and Apache Flink. The selected candidate will play a pivotal role in designing, implementing, and optimizing scalable cloud-based data warehouse solutions, including an Operational Data Store (ODS) to serve as a speed layer for advanced analytics and business intelligence initiatives.

What You’ll Do

• Design, develop, test, and maintain scalable Java services and distributed applications.
• Build and optimize real-time data streaming pipelines using Apache Flink, Apache Pulsar, and Apache Kafka.
• Develop reliable event-driven services with strong attention to throughput, latency, resiliency, ordering, replay, and fault tolerance.
• Design and implement an Operational Data Store (ODS) layer using GraphQL and PostgreSQL.
• Develop GraphQL APIs and services that provide secure, efficient, and well-structured access to operational data.
• Design relational schemas, queries, indexes, and data-access patterns for PostgreSQL.
• Integrate streaming and operational data services with Databricks and cloud-based data platforms.
• Deploy and operate applications in Microsoft Azure using cloud-native architecture and services.
• Implement automated build, test, deployment, and release processes through CI/CD pipelines.
• Apply software engineering best practices, including code reviews, automated testing, observability, documentation, and secure development.
• Troubleshoot complex issues across application, streaming, database, and cloud environments.
• Participate in technical design discussions and contribute to architecture decisions.
• Mentor other engineers and help establish engineering standards and reusable patterns.
• Use generative AI tools and effective prompting techniques to accelerate development, troubleshoot issues, create documentation, and improve engineering productivity while applying appropriate security and data-handling practices.

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