AI Data Platform Engineer

Accountabilities

• • Define the target-state architecture for an enterprise data platform across ingestion, storage, processing, governance, and consumption layers.
  • Establish technical standards for data modeling, schema evolution, partitioning, file formats, storage organization, and data lifecycle management.
  • Architect modern lakehouse, warehouse, and streaming solutions using technologies such as Snowflake, Databricks, BigQuery, Redshift, Iceberg, Delta Lake, or Hudi.
  • Design end-to-end batch and streaming data pipelines that balance performance, latency, reliability, cost, and maintainability.
  • Lead the integration of data governance, lineage, cataloging, and discovery capabilities using platforms such as Collibra, Alation, Atlan, Unity Catalog, or DataHub.
  • Define security architectures covering identity-aware access, encryption, masking, and row- and column-level controls.
  • Partner with ML, BI, product, analytics, and business teams to ensure the platform meets downstream data consumption requirements.
  • Establish data contract and data product principles that promote clear ownership, quality, scalability, and effective separation between producers and consumers.
  • Lead architecture reviews, evaluate proposed designs, and provide technical guidance to engineering and architecture teams.
  • Drive data-platform cost optimization, capacity planning, high availability, disaster recovery, and multi-region strategies for critical assets.
  • Mentor data engineers and architects while promoting modern platform standards, architectural consistency, and emerging best practices.
  • Produce architecture documentation, including context diagrams, architecture decision records, reference architectures, and reusable design patterns.
  • Monitor developments across data-platform technologies, vendors, research, and open-source ecosystems to inform future architectural decisions.

  Requirements

  • Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related discipline.
  • 8+ years of experience in data engineering, including substantial experience in data architecture or platform architecture roles.
  • Deep expertise with at least two major data platforms, such as Snowflake, Databricks, BigQuery, or Redshift.
  • Strong understanding of modern lakehouse architectures, table formats, distributed data processing, and streaming systems.
  • Production-scale experience with technologies such as Spark, Flink, or Kafka.
  • Strong data modeling capabilities across dimensional, normalized, and data-vault approaches.
  • Demonstrated experience implementing data governance, lineage, cataloging, quality, and ownership frameworks.
  • Solid knowledge of cloud platforms, networking, identity and access management, security, and data-platform cost optimization.
  • Proven track record of leading complex, cross-functional data-platform initiatives from architecture through implementation.
  • Strong communication, facilitation, presentation, and stakeholder-management skills, with the ability to translate complex technical concepts for both technical and business audiences.
  • Experience with data mesh or data product architectures is preferred.
  • Familiarity with semantic-layer technologies such as dbt Semantic Layer, Cube, or LookML is advantageous.
  • Experience working in regulated environments involving data residency, compliance, or audit requirements is a plus.
  • Cloud or platform certifications across Snowflake, Databricks, AWS, Azure, or GCP are advantageous.
  • Public speaking, technical writing, or contributions to the data architecture community are a plus.

  Benefits

  • Competitive salary: $135,000–$170,000 annually.
  • Remote flexibility: 100% remote position within the United States.
  • Employment type: Full-time, direct W-2 position.
  • Career growth: Opportunity to shape enterprise-scale data architecture and influence major technology initiatives.
  • Technical impact: Work across modern cloud data platforms, lakehouse architectures, streaming technologies, governance, and data products.
  • Leadership opportunities: Mentor engineers and architects while establishing technical standards and architectural best practices.
  • Collaborative environment: Partner with engineering, analytics, ML, product, and business stakeholders across the organization.
  • Innovation-focused work: Stay at the forefront of evolving data-platform technologies, cloud services, and open-source developments.