Director of Engineering, Data

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.