Lead Data Platform Architect

We are seeking a Lead Data Platform Architect to join a physical AI program building autonomous construction machines that operate unsupervised at customer sites, for a global heavy-equipment manufacturer and long-standing pioneer in autonomous machinery. In this role, you will lead the data platform workstream between the perception, connectivity and robotics teams, owning a largely greenfield architecture that carries terabyte-scale sensor data from the machines into versioned datasets for vision-language-action (VLA) model training and simulation, and setting the patterns other teams build on.

Responsibilities

• Sensor-to-Dataset Architecture: Design the path from on-machine upload through landing, curation, annotation and training-set creation for camera images, LiDAR and radar point clouds, GNSS/IMU and machine telemetry, across a fleet scaling to 500+ machines.
• Storage & Metadata Registry: Define storage for images, video and point clouds, with a metadata registry for every asset (machine, sensor, calibration version, timestamps, pose, landing layer), and support both batch and near-real-time streaming consumption for downstream services.
• Lineage, Versioning & Governance: Set up lineage, cataloging, versioning, lifecycle and retention policies end to end, from raw sensor logs to each training and simulation dataset, including how synthetic data is stored, versioned and mixed with real data.
• Data Contracts & Edge-to-Cloud: Agree data contracts and metadata standards for next-generation machine kits, and shape edge data reduction, upload and encryption from kit to cloud together with the perception and connectivity teams.
• Hands-On Leadership: Produce architecture artifacts engineers can implement right away, refine them from implementation feedback using AI coding agents, and guide the EU-based Data Platform Architect on the team.

Requirements

• 10+ years of experience in data architecture or data engineering, including platforms at scale.
• Edge & Telemetry Data: Proven work with high-volume device, IoT or telemetry data from edge to cloud at terabyte-per-day scale or more.
• Unstructured & Binary Data: Experience with images, video or point clouds on object storage, backed by metadata catalogs and registries.
• Ingestion & Lakehouse: Strong command of streaming and batch ingestion (Kafka, Kinesis or CDC) and lakehouse formats (Parquet, Iceberg or Delta).
• Lineage, Governance & ML Datasets: Practical data lineage, versioning and governance beyond table cataloging, plus ML dataset preparation with splits, holdouts and reproducible versions.
• Cloud & Engines: Hands-on with at least one major cloud (AWS S3, Azure or GCP) and engines such as Databricks or Snowflake, without being tied to one vendor; Python.
• AI-Assisted Delivery: Comfort using AI coding tools to turn architecture into working software quickly.

Nice to have

• Autonomy Data Pipelines: Experience with autonomous vehicle, robotics or ADAS data (ROS bags or MCAP, sensor logs, drive or scene data).
• Synthetic Data & Labeling: Work with synthetic or simulated imagery and sensor data, and with annotation pipelines including auto-labeling.
• Edge Optimization & Encryption: Experience with edge data reduction strategies and end-to-end encryption from edge to cloud.
• Security & Access Control: Knowledge of security and compliance frameworks, plus role-based and attribute-based access control across query, reporting and AI layers.
• Travel Readiness: Willingness to travel to client sites when needed.

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office

About Us Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.