Grid Dynamics

Senior Data Platform Architect

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C-Level / ManagerHybryda·WarszawaFull-time·B2B

#461513·Dodano 12 dni temu·0

Źródło: justjoin.it

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Tech Stack / Keywords

Python Spark Databricks

Podsumowanie oferty

Own part of a largely greenfield data platform for a physical AI program building autonomous construction machines. Turn multimodal sensor data into trusted, versioned datasets for vision-language-action model training and simulation, using Python, Spark or Databricks.

Wymagania

Proven commercial experience in data architecture or data engineering on platforms at scale, with hands-on delivery.
Experience with images, video or point clouds on object storage, backed by metadata catalogs and registries.
Practical experience with curation, dataset splits, holdouts and reproducible dataset versions.
Hands-on lineage and versioning from raw data to training datasets using LakeFS, DVC, Delta or equivalent.
Strong knowledge of Parquet, Iceberg or Delta, plus batch and streaming ingestion with Kafka or Kinesis.
Hands-on work with at least one major cloud (AWS S3, Azure or GCP), Spark or Databricks, and Python.
Comfort using AI coding tools to turn architecture into working software quickly.

Nice to have:

Experience with autonomous vehicle, robotics or ADAS data (ROS bags or MCAP, sensor logs, drive or scene data).
Experience building annotation and labeling pipelines, including auto-labeling.
Work with synthetic or simulated imagery and sensor data for training or testing.
Hands-on use of MLflow or similar tools for experiment and artifact tracking.

Obowiązki

Curation & Annotation Flow: Design the end-to-end flow from landed sensor data to training-set creation for a data-collection fleet scaling to 500+ machines.
Metadata Registry & Cataloging: Build the metadata registry for every asset and define the cataloging approach used by ML and robotics engineers.
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.

Benefity

Flexible schedule.
Benefits package including medical insurance and sports.
Corporate social events.
Professional development opportunities.
Well-equipped office.

Elastyczne godziny

Opieka zdrowotna

Karta sportowa

Spotkania integracyjne

Inne informacje

Willingness to travel to client sites when needed.

Grid Dynamics

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