Lead Data Engineer.

AMD AMD · Semiconductors · Gdansk, Poland · Engineering

Lead Data Architect responsible for defining, designing, and driving the architecture of next-generation data platforms, with a strong emphasis on supporting AI/ML workloads, feature stores, pipelines, and MLOps. The role involves evaluating and recommending modern data technologies, ensuring robustness, quality, and security, and collaborating with data scientists and ML engineers to translate AI/ML requirements into scalable, production-ready data architectures.

What you'd actually do

  1. Define, document, and govern end‑to‑end data architectures, including data lakes / lake houses, analytics platforms, integration patterns, and supporting infrastructure, ensuring alignment with product roadmaps and business objectives.
  2. Translate business, analytics, and product requirements into actionable data architecture designs, including logical and physical data models, platform specifications, interface contracts, and architectural decision records.
  3. Evaluate, select, and recommend modern data technologies and architectural patterns (cloud services, storage engines, compute frameworks, data formats, governance tools) to optimize scalability, performance, reliability, and cost efficiency.
  4. Drive the design and evolution of core data platform components, with a strong emphasis on robustness, data quality, security, fault tolerance, and operational excellence.
  5. Act as the architectural escalation point for complex data platform issues, providing guidance and resolution across ingestion, storage, processing, analytics, and governance layers.

Skills

Required

  • 12+ years of experience in data architecture, data engineering, or large‑scale data platform design, with at least 5 years in a senior, lead, or principal role driving architecture.
  • Strong expertise in designing data architectures that support AI/ML workloads, including feature stores, pipelines, batch and streaming data processing, data layers.
  • Hands‑on experience with modern AI/ML and data platforms, such as cloud‑native lake house architectures and ML platforms or tooling for experimentation, training, deployment, and monitoring.
  • Deep understanding of MLOps and ML lifecycle management, including data versioning, experiment tracking, model governance, CI/CD for ML, and production monitoring.
  • Proficiency in programming and data languages used in AI/ML ecosystems (e.g., Python, SQL), with the ability to reason about performance, scalability, and reliability of data pipelines.
  • Experience working with both structured and unstructured data, including datasets used for advanced analytics, machine learning, and generative AI use cases.
  • Strong knowledge of data governance, security, and compliance principles as applied to AI systems, including data quality, lineage, access control, privacy, and responsible AI considerations.
  • Solid understanding of cloud infrastructure and distributed systems, including storage, compute, networking, and cost‑optimization strategies for AI‑driven data platforms.
  • Demonstrated ability to collaborate closely with data scientists, ML engineers, platform teams, and business stakeholders, translating AI/ML requirements into scalable, production‑ready data architectures.
  • Excellent written and verbal communication skills in English, with proven experience presenting complex AI/data architecture concepts to both technical and executive audiences.
  • PhD or MSc degree in Computer Engineering, Software Engineering, Computer Science

What the JD emphasized

  • AI/ML workloads
  • feature stores
  • ML platforms
  • MLOps
  • generative AI use cases
  • Responsible AI

Other signals

  • AI/ML workloads
  • feature stores
  • ML platforms
  • MLOps
  • generative AI use cases
  • Responsible AI