{"id":171623,"url":"https://alion.io/job/gridware-director-of-engineering-data","title":"Director of Engineering, Data","company":{"id":173845,"name":"Gridware","domain":"gridware.io","url":"https://alion.io/company/gridware-2","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":93,"open_postings":11,"ghost_share":0,"stale_share":0.091,"repost_share":0,"time_to_fill_p50_days":134,"computed_at":"2026-10-26T05:57:25Z"}},"role":"Leadership","role_family":"Leadership","seniority":"head","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":260000,"max":280000,"currency":"USD","period":"year","gross":null,"usd_annual":280000},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Kinesis","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Databricks","optional":false},{"name":"Machine Learning","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-08-25T17:29:29Z","employer_posted_date":"2026-08-25","last_verified_at":"2026-10-26T22:11:45Z","board_verified":true,"closed_at":null,"days_open":62,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":61},"description":"Role Overview\nWe’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.\nYou’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.\nResponsibilities\nPlatform architecture and delivery\nOwn the data platform end to end: ingestion, transformation, raw and sanitized data layers, and overall data architecture.\nDrive data quality, observability, lineage, and governance across the organization.\nAdvance legacy migrations and step-change improvements to data platforms without disrupting the teams already depending on them.\nApplied data science\nManage a small, cross-functional data science team focused on rapid assessment of new data problems to determine valuable investments.\nSet the bar for rigor on early-stage analysis. Turn ambiguous questions into scoped, decision-ready answers for stakeholders across the company.\nEnablement and adoption\nOwn the tooling, patterns, and support that let business domains build and maintain their own analytics and reporting under a federated ownership model.\nBuild the data foundations that machine learning and applied research depend on.\nDrive analytics enablement for both technical and non-technical teams across the company.\nOrg building\nHire and design the data organization as it grows over the next year.\nBuild and grow a world-class team of data engineering leaders.\nBalance incubation work against platform goals, and grow both sides of the org as the business scales\nRequired Skills\n10+ years in data engineering or data platform roles, including 5+ years in engineering management roles, currently or most recently managing managers.\nHands-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.\nBackground in high-volume, machine-generated data: telemetry, sensor, IoT, observability, or logs, rather than primarily transactional or clickstream data.\nExperience running a data platform with real internal business stakeholders beyond engineering.\nExperience operating in a federated or domain-ownership model, with a clear point of view on what worked and what didn't.\nExperience building or substantially rebuilding platform capability, not only maintaining a mature platform.\nExperiencing leading data science/analytics, including efforts where the problem isn't well-defined.\nBonus Skills\nBackground in physical-world or safety-critical systems: energy, IoT, robotics, autonomy, or aerospace.\nExperience with edge/cloud architecture under power or bandwidth constraints.\nExperience migrating an organization off a fragmented legacy state while it stayed live.\nExperience scaling with a company 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