Summary
Written in English from the original postingGridware is hiring a Director of Engineering, Data in San Francisco, CA. The role leads company-wide data platform architecture, ingestion, transformation, governance, and legacy migrations. It also builds data engineering and data science teams while enabling domain-owned analytics and machine learning foundations.
Job description
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.
