Description

Key Responsibilities

• Define the target-state architecture of the enterprise data platform, including ingestion, storage, processing, and consumption layers.
• Establish standards for data modeling, schema evolution, partitioning, file formats, and storage organization.
• Architect lakehouse, warehouse, and streaming patterns leveraging technologies such as Snowflake, Databricks, BigQuery, Redshift, Iceberg, Delta Lake, or Hudi.
• Design end-to-end data pipelines that balance latency, cost, reliability, and maintainability across batch and streaming workloads.
• Lead the integration of governance, lineage, and catalog tools such as Collibra, Alation, Atlan, Unity Catalog, or DataHub.
• Define security architecture including row- and column-level controls, masking, encryption, and identity-aware access patterns.
• Partner with ML, BI, and product teams to ensure platform capabilities align with downstream consumption needs.
• Establish data contract and data product principles to drive ownership, quality, and decoupling between producers and consumers.
• Lead architecture reviews and provide guidance on pipeline and warehouse design proposals across teams.
• Drive cost optimization and capacity planning across the data platform estate.
• Design disaster recovery, multi-region, and high-availability strategies for critical data assets.
• Mentor data engineers and architects on platform standards and emerging best practices.
• Produce architecture artifacts including context diagrams, decision records, and reference patterns.
• Stay current with data platform research, vendor offerings, and open-source ecosystem developments.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related field.
• 12 or more years of experience in data engineering, with significant time in architecture roles.
• Deep expertise across at least two major data platforms such as Snowflake, Databricks, BigQuery, or Redshift.
• Strong understanding of lakehouse architectures, modern table formats, and streaming systems.
• Hands-on experience with Spark, Flink, or Kafka at production scale.
• Strong data modeling expertise across dimensional, normalized, and data-vault patterns.
• Experience implementing governance, lineage, and catalog capabilities.
• Solid grasp of cloud platforms, networking, identity, and cost optimization.
• Excellent communication, facilitation, and stakeholder management skills.
• Track record of leading large data platform initiatives across teams.

Preferred Qualifications

• Experience with data mesh or data product architectures.
• Familiarity with semantic layers such as dbt Semantic Layer, Cube, or LookML.
• Exposure to regulated industries with strict data residency or audit requirements.
• Cloud or platform certifications (Snowflake, Databricks, AWS, Azure, GCP).
• Public talks or writing on data architecture

Key Skills

Education

Any Gradute