Sr. Distributed Systems Engineer, Energy Service Engineering

What to Expect

We're seeking a highly skilled and collaborative Senior distributed systems engineer to architect and implement a cutting-edge data platform while leading the development of streaming data pipelines, data lake, and OLAP infrastructure to support the growth of our energy data platform including Industrial, Residential, Supercharger, and Solar products. You'll design large-scale data systems that integrate multiple sources including deployed fleets, internal applications, and data warehouses while developing robust monitoring and alerting infrastructure.

Your role involves building efficient batch and streaming applications that power business intelligence and support ML engineers with feature engineering infrastructure. You'll create analytics capabilities to track energy products throughout their lifecycle, from creation to deployment, usage, maintenance, and replacement.

Working cross-functionally with teams across our energy divisions, you'll leverage your expertise in data engineering to build scalable solutions that enable rapid development and deployment of data products and drive informed decision-making.

What You’ll Do

• Design, develop, and operate distributed data systems that process streaming and batch workloads at terabyte- to petabyte-scale across Tesla's global energy fleet, against sub-minute end-to-end latency SLAs
• Build streaming pipelines on Apache Spark Structured Streaming and Kafka with exactly once correctness and recovery guarantees, and evaluate complementary stream-processing engines (Apache Flink, Kafka Streams, Akka Streams, or Apache Beam) where it’s the right framework
• Architect high-throughput ingest paths from Kafka into the Lakehouse and into low-latency OLAP stores (Click House, Druid, Pinot), including schema and codec design (Protobuf, Avro, Flatbuffers) and backpressure handling
• Architect the Lakehouse on Delta Lake / Apache Iceberg / Apache Hudi with streaming-aware partitioning, compaction, CDC/upsert, and schema-evolution strategies; develop aggregate, summary, and real-time feature tables for engineering, analytics, and ML teams across product lines and geographies
• Drive complex problems around scalability, reliability, performance, and cost to resolution including stateful-streaming concerns like watermarks, late-arriving data, state backends, and checkpoint/savepoint recovery
• Own CI/CD, monitoring, alerting, SLOs, and on-call for data applications treating pipelines as production services and provide technical leadership in close partnership with ML engineers, analytics, and product engineering
• Build self-service tooling that lets cross-functional teams discover and consume streaming and batch data faster, and research and incorporate emerging stream-processing and data infrastructure into the platform

What You’ll Bring

• Strong data engineering background with expertise in at least two programming languages (Python, Scala, Java, or Rust)
• Several years of industry experience designing, building, and operating large-scale distributed data systems in production, with at least 3+ years focused on data engineering or analytics engineering
• Production experience with at least one stream-processing engine (Apache Spark Structured Streaming, Apache Flink, Kafka Streams, Akka Streams, or Apache Beam) operating at terabyte-scale throughput or sub-minute latency
• Deep working knowledge of Apache Kafka (or equivalent log/broker such as Pulsar or Kinesis) as a production backbone: partitioning, consumer-group semantics, delivery guarantees, schema management, and backpressure handling
• Optimizing end-to-end streaming latency from source to sink while balancing throughput, cost, and correctness guarantees
• Solid grasp of streaming fundamentals: event-time vs. processing-time, watermarks, exactly once semantics, stateful operators, and checkpoint/recovery
• Hands-on experience with modern data lake technologies (Delta Lake, Apache Iceberg, or Apache Hudi) and containerization/orchestration (Docker, Kubernetes)