Job description

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 Overview We’re looking for a Director of Engineering to lead Gridware’s company-wide data platforms. This is a hands-on, engineering leadership role: you’ll set the architecture, drive the roadmap, and build the team for a platform that’s live and in production today, with real customers, a legacy migration in flight, and a federated ownership model across a large number of business domains. You’ll be working with novel, never-before-seen data: high-frequency mechanical, acoustic, electrical, and environmental signals from tens of thousands of power-constrained edge devices with intermittent connectivity, where ground truth comes from line crews in the field rather than a labeling vendor. This role will define new data problems that can be solved and change what the rest of the organization believes is possible with Gridware data. Responsibilities Platform architecture and delivery • Own the data platform end to end: ingestion, transformation, raw and sanitized data layers, and overall data architecture. • Drive data quality, observability, lineage, and governance across the organization. • Advance legacy migrations and step-change improvements to data platforms without disrupting the teams already depending on them. Applied data science • Manage a small, cross-functional data science team focused on rapid assessment of new data problems to determine valuable investments. • Set the bar for rigor on early-stage analysis. Turn ambiguous questions into scoped, decision-ready answers for stakeholders across the company. Enablement and adoption • Own the tooling, patterns, and support that let business domains build and maintain their own analytics and reporting under a federated ownership model. • Build the data foundations that machine learning and applied research depend on. • Drive analytics enablement for both technical and non-technical teams across the company. Org building • Hire and design the data organization as it grows over the next year. • Build and grow a world-class team of data engineering leaders. • Balance incubation work against platform goals, and grow both sides of the org as the business scales Required Skills • 10+ years in data engineering or data platform roles, including 5+ years in engineering management roles, currently or most recently managing managers. • Hands-on experience with modern lakehouse and streaming tooling (e.g., Databricks, Kafka, Kinesis, CDC pipelines, Airflow, Terraform) and able to contribute heavily to an architecture review with the team. • Background in high-volume, machine-generated data: telemetry, sensor, IoT, observability, or logs, rather than primarily transactional or clickstream data. • Experience running a data platform with real internal business stakeholders beyond engineering. • Experience operating in a federated or domain-ownership model, with a clear point of view on what worked and what didn't. • Experience building or substantially rebuilding platform capability, not only maintaining a mature platform. • Experiencing leading data science/analytics, including efforts where the problem isn't well-defined. Bonus Skills • Background in physical-world or safety-critical systems: energy, IoT, robotics, autonomy, or aerospace. • Experience with edge/cloud architecture under power or bandwidth constraints. • Experience migrating an organization off a fragmented legacy state while it stayed live. • Experience scaling with a company through a period of significant growth. 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! Gridware Technologies Inc. is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to any characteristic protected by applicable federal, state, or local law. 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

Requirements

Must-haves

Platform architecture and delivery
Own the data platform end to end: ingestion, transformation, raw and sanitized data layers, and overall data architecture.
Drive data quality, observability, lineage, and governance across the organization.
Advance legacy migrations and step-change improvements to data platforms without disrupting the teams already depending on them.
Applied data science
Manage a small, cross-functional data science team focused on rapid assessment of new data problems to determine valuable investments.
Set the bar for rigor on early-stage analysis. Turn ambiguous questions into scoped, decision-ready answers for stakeholders across the company.
Enablement and adoption
Own the tooling, patterns, and support that let business domains build and maintain their own analytics and reporting under a federated ownership model.
Build the data foundations that machine learning and applied research depend on.
Drive analytics enablement for both technical and non-technical teams across the company.
Org building
Hire and design the data organization as it grows over the next year.
Build and grow a world-class team of data engineering leaders.
Balance incubation work against platform goals, and grow both sides of the org as the business scales
10+ years in data engineering or data platform roles, including 5+ years in engineering management roles, currently or most recently managing managers.
Hands-on experience with modern lakehouse and streaming tooling (e.g., Databricks, Kafka, Kinesis, CDC pipelines, Airflow, Terraform) and able to contribute heavily to an architecture review with the team.
Background in high-volume, machine-generated data: telemetry, sensor, IoT, observability, or logs, rather than primarily transactional or clickstream data.
Experience running a data platform with real internal business stakeholders beyond engineering.
Experience operating in a federated or domain-ownership model, with a clear point of view on what worked and what didn't.
Experience building or substantially rebuilding platform capability, not only maintaining a mature platform.
Experiencing leading data science/analytics, including efforts where the problem isn't well-defined.
Background in physical-world or safety-critical systems: energy, IoT, robotics, autonomy, or aerospace.

Nice-to-haves

Bonus Skills

Application prep

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Keywords to mirror

restmachine learning

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

Parental Leave