Senior Data Platform Architect
First seen by Alion on Oct 19, 2026.
We are seeking a Senior 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 work alongside the Data Platform Architect (Lead) and the perception, kit and robotics teams, owning the part of a largely greenfield platform that turns raw multimodal sensor data into trusted, versioned datasets for vision-language-action (VLA) model training and simulation.
Essential functions
Curation & Annotation Flow: Design the end-to-end flow from landed sensor data (camera images, LiDAR and radar point clouds, GNSS/IMU and machine telemetry) to training-set creation, for a data-collection fleet scaling to 500+ machines.
Metadata Registry & Cataloging: Build the metadata registry for every asset (machine, sensor, calibration version, timestamps, pose, landing layer) and define the cataloging approach that ML and robotics engineers rely on.
Lineage, Versioning & Lifecycle: Set up lineage, versioning, lifecycle and retention from raw sensor logs to each training dataset, so every model can be traced back to its source data.
Simulation-Ready & Synthetic Data: Define how real data becomes simulation-ready and how synthetic data is stored, versioned and mixed with real data.
Data Contracts & Implementable Architecture: Agree data contracts with the perception and kit teams, and produce architecture artifacts engineers can start building on right away, working hands-on with AI coding agents.
Qualifications
Proven commercial experience in data architecture or data engineering on platforms at scale, with hands-on delivery.
Unstructured & Binary Data: Experience with images, video or point clouds on object storage, backed by metadata catalogs and registries.
ML Dataset Preparation: Practical experience with curation, dataset splits, holdouts and reproducible dataset versions.
Lineage & Versioning: Hands-on lineage and versioning from raw data to training datasets using LakeFS, DVC, Delta or equivalent.
Lakehouse & Ingestion: Strong knowledge of Parquet, Iceberg or Delta, plus batch and streaming ingestion with Kafka or Kinesis.
Cloud & Engines: Hands-on work with at least one major cloud (AWS S3, Azure or GCP), Spark or Databricks, and Python.
AI-Assisted Delivery: Comfort using AI coding tools to turn architecture into working software quickly.
Would be a plus
Autonomy Data Pipelines: Experience with autonomous vehicle, robotics or ADAS data (ROS bags or MCAP, sensor logs, drive or scene data).
Annotation & Labeling: Experience building annotation and labeling pipelines, including auto-labeling.
Synthetic Data: Work with synthetic or simulated imagery and sensor data for training or testing.
ML Experiment Tracking: Hands-on use of MLflow or similar tools for experiment and artifact tracking.
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.
Tech stack
- Python
- Spark
- Databricks