Data Quality Engineering Lead - Questar Auto Technologies

Company Overview

Questar Auto Technologies is an AI-powered fleet intelligence company transforming vehicle health management for commercial fleets. Drawing on 15+ years of connected vehicle data, our AI monitors hundreds of thousands of vehicles across four continents and predicts component failures weeks before they happen. We process tens of trillions of data points per year from our own in-vehicle devices and third-party telematics platforms.

Position Overview

We are seeking a hands-on Data Quality Engineering Lead to own the correctness and reliability of the data behind every the telematics indicators, KPIs, Health Scores and AI recommendations our customers act on, consistently across the entire Questar platform, including Fleet Health and Fleet Operations.

The immediate priority is the ingestion path: from the Questar telematics device and third-party telematics ingest APIs to the entry point of the GenAI pipeline (the “landing zone”), across streaming and static data. Direct ownership ends at the landing zone; beyond it, the role requires end-to-end understanding of how this data flows through to what users see in the Questar app, so upstream quality translates into accurate customer-facing outputs. The goal is to actively improve data correctness, not just monitor and report on it.

Key Responsibilities

Data Investigation , Root-Cause Analysis & Resolution

• Become the inside expert when it comes to thedata flow from the initial ingest point to the “landing zone”, including the telematics device configuration, telematics device’s data transformation capabilities and limitations, back-end's data normalization, validation and enrichment flows.
• Understand not only what data is ingested and how, but why: the business logic behind each data element, what it is used for across the product, and the downstream impact of any change, connecting the dots across the solution.
• Investigate data issues hands-on with expert SQL and Python, down to the first bad record, when a vehicle stops reporting, a feed goes partial or two sources disagree.
• Apply a systematic root-cause method (device/firmware vs. ingestion vs. transformation vs. model) and own each issue end-to-end, from investigation through solution design, implementation and permanent resolution, using AI and Analytics tools (e.g., Claude code) to move faster.

• Build automated data validation suites in production and embed them as blocking gates in pipelines and CI/CD.
• Own freshness, latency, volume and anomaly monitoring per source and per fleet; run quality scorecards with SLAs and a data incident process.

• Define data quality requirements and acceptance criteria for new features, integrations and model releases before build starts; set data contracts with partners and device firmware owners.
• Drive permanent fixes through teams you don’t manage (R&D, platform, external partners, data vendors), and communicate data quality in business terms to leadership and customer-facing teams.
• Ensure data consistency across Questar app modules (Fleet Health and Fleet Operations)
• Over time, extend coverage into the ML and GenAI pipeline: model-input drift, stale data never shown as live, and no double-counting of overlapping signals.

• Define and own data quality KPIs: automated-check coverage to "the landing zone”, time to detect and resolve incidents, freshness SLA attainment, and customer-reported data issues.

Qualifications

Education

• Bachelor’s degree in Computer Science, Engineering, Data Science or a related field, or equivalent practical experience.

• 6+ years in data engineering, analytics engineering, data quality or data QA automation at Senior / Staff level;
• Production experience building automated data validation and quality gates, including validation at ingestion from devices and third-party APIs.
• High-volume time-series, IoT or event data experience, and a track record of root-cause analysis that led to permanent fixes.
• Advantage: telematics or vehicle data (J1939 / OBD-II), VIN data extraction, ML data validation, Data Privacy Rules (GDPR) and PII mapping and classification, and scale-up platform migrations.

• Expert SQL and strong Python; Big data / Spark / lakehouse / postgresql platforms.
• dbt tests, Soda or Deequ, and CI/CD for data; AI power-user with co-pilots and agents.
• Full stack: Embedded and Cloud environments.

• Strong ownership mindset and excellent communication with technical and non-technical audiences.
• Thrives in ambiguity; leads by doing, with potential to grow a small data quality function.

What We Offer

• A high-impact role where data quality directly shapes product trust and growth, in a fast-evolving, AI-driven fleet technology space.

If you are passionate about making data right, not just measuring it, we want to hear from you! Apply now and join us on our journey to redefine Fleet Health.