Senior Data Engineer

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, Snowflake, 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.

• →Develop cloud data warehouse solutions leveraging Databricks and Kubernetes.
• →Implement robust data pipelines to ingest, process, and store IoT data from diverse sources.
• →Integrate and manage large-scale datasets using Apache Iceberg for efficient, reliable, and scalable data lake operations.
• →Build an Operational Data Store (ODS) to function as a speed layer, enabling rapid access for near real-time use Cases.
• →Collaborate with data engineers, analysts, and business stakeholders to understand requirements and deliver scalable solutions.
• →Optimize data models, storage, and compute resources for performance and cost efficiency.
• →Ensure data quality, security, and compliance across all cloud data platforms.
• →Setting up monitoring tools and dashboards to track pipeline health, diagnose issues, and optimize performance.
• →Stay current with industry trends and best practices in data management, ODS technologies, and API development.

• Bachelor’s in software engineering, Computer Science, Information Technology, or a related field or equivalent experience.
• 7+ years experience designing, developing, and supporting enterprise applications.
• Proven in implementing cloud data warehouses using Databricks, Snowflake or any other data platform.
• Hands-on experience with Apache Flink or Spark for data lake pipelines.
• Experience designing and building Operational Data Stores (ODS) as speed layers for analytic environments.
• Experience with IoT data ingestion, processing, and analytics.
• Strong proficiency in SQL, Python, Java and ETL tools.
• Proven cloud experience and strong familiarity with at least one cloud platform (Microsoft Azure - preferred, AWS, GCP).
• Experienced in proactive issue detection leveraging anomaly detection techniques, with a strong focus on monitoring pipeline performance, ensuring system reliability, and identifying bottlenecks through observability metrics.
• Good understanding and experience with CI/CD practices.

• Experience with Databricks platform and Databricks certifications.

• Experience with real-time data streaming technologies (e.g., Kafka, Spark Streaming, Flink).

• Knowledge of data governance and security best practices.

• OCPP Protocol expertise

This job may be eligible for relocation benefits.

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