Gridware is a technology company focused on protecting and enhancing the electrical grid. We pioneered a groundbreaking new class of grid management called active grid response (AGR), focused on monitoring the electrical, physical, and environmental aspects of the grid that affect reliability and safety. Gridware’s advanced active grid response platform uses high-precision mechanical sensors to detect potential issues early, enabling proactive maintenance and fault mitigation. This comprehensive approach helps improve safety, reduce outages, and ensure the grid operates efficiently. The company is headquartered in San Francisco, California, and is backed by climate-tech and Silicon Valley investors. For more information, please visit www.Gridware.io.
About Gridware
Gridware is a San Francisco-based technology company dedicated to protecting and enhancing the electrical grid. We pioneered a groundbreaking new class of grid management called active grid response (AGR), focused on monitoring the electrical, physical, and environmental aspects of the grid that affect reliability and safety. Gridware’s advanced Active Grid Response platform uses high-precision sensors to detect potential issues early, enabling proactive maintenance and fault mitigation. This comprehensive approach helps improve safety, reduce outages, and ensure the grid operates efficiently. The company is backed by climate-tech and Silicon Valley investors. For more information, please visit www.Gridware.io.
Role Description
As a Staff Data Engineer / Data Architect at Gridware, you will define and build the architecture of our most critical data systems, spanning real-time streaming pipelines, Lakehouse governance, telemetry ingestion, and cross-domain data models. This role sits at the top of the technical IC track and requires deep expertise in distributed data systems, IoT telemetry flows, and architectural strategy.
You will partner across Firmware, Software, DevOps, and Data Science to establish Gridware’s long-term data platform vision. Your work will directly shape how sensor and grid event data flows through our system, enabling fault detection, wildfire risk mitigation, and real-time grid intelligence at scale.
This describes the ideal candidate; many of us have picked up this expertise along the way. Even if you meet only part of this list, we encourage you to apply!
Benefits
Health, Dental & Vision (Gold and Platinum with some providers plans fully covered)
Paid parental leave
Alternating day off (every other Monday)
“Off the Grid”, a two week per year paid break for all employees.
Commuter allowance
Company-paid training
Responsibilities
Architect and scale high-throughput ingestion systems for sensor telemetry, device metadata, and real-time grid events.Design end-to-end data platform architecture, including Lakehouse structures, governance layers, data domains, and lineage standards.Lead the evolution of our Event Streaming Platform, including schema strategy, Protobuf evolution, retention policies, and reliability models.Develop fault-tolerant, self-healing data services that maintain availability during storms, outages, and high-volume event bursts.Drive multi-quarter, multi-team data initiatives and partner across engineering to establish consistent ingestion contracts, schemas, and standards.Required Skills
10+ years of experience in Data Engineering, Data Architecture, or Distributed Systems roles.Expert-level knowledge of event streaming (Kafka/Kinesis), distributed compute (Spark/Databricks), and Lakehouse architectures.Demonstrated experience designing large-scale, real-time telemetry or IoT data pipelines with strong reliability requirements.Deep understanding of partitioning, backpressure, consistency models, and distributed system internals.Proven ability to own complex systems end-to-end and influence technical strategy across teams.Bonus Skills
Experience with industrial IoT, sensor networks, or utility/energy data.Expertise in Kubernetes, service mesh patterns, or cloud infrastructure for data-intensive systems.Background designing data governance frameworks or domain ownership models.Open-source contributions, publications, or talks in distributed systems or data platform architecture.Experience building high-SLA ingestion platforms for real-time monitoring or alerting systems.