Lead Data Platform Architect - Snowflake DB/Databricks

Job Title: Lead Data Platform Architect (Snowflake / Databricks)

We are seeking a visionary and highly technical Lead Data Platform Architect with 10 to 15 years of experience to design, build, and scale our next-generation enterprise data platform. This role is designed for a senior leader who can bridge the gap between high-level business strategy and deep technical execution. In this role, you will be the primary architect responsible for defining the blueprint of our modern data stack, leveraging the strengths of both Snowflake and Databricks. You will lead the migration from legacy systems, implement robust data mesh/lakehouse architectures, and ensure that our data platforms are secure, highly performant, and cost-optimized to support advanced analytics, AI/ML, and real-time data streaming.

Core Responsibilities:

- Define the enterprise data strategy and design scalable, resilient Lakehouse architectures using Databricks (Delta Lake) and Cloud Data Warehouses (Snowflake).

- Serve as the subject matter expert (SME) across the organization, guiding engineering teams on best practices for data modeling, ingestion pipelines, and storage optimization.

- Lead complex data migration strategies from legacy on-premise setups or traditional data warehouses to a unified Snowflake/Databricks cloud ecosystem.

- Oversee query performance tuning, indexing, partitioning, and compute resource management (e.g., Snowflake virtual warehouses, Databricks clusters) to minimize latency and control cloud spend.

- Architect robust access control policies (RBAC/ABAC), data masking, row/column-level security, and audit logging to ensure strict compliance with global data standards (GDPR, HIPAA, etc.).

- Design automated deployment workflows for data pipelines, infrastructure as code (IaC) via Terraform, and database version control (e.g., Schemachange, Liquibase).

- Collaborate with product managers, data scientists, business analysts, and executive leadership to translate business objectives into high-performing data products.

Key Technical Skills & Requirements:

- Total Experience: 10 to 15 years in Data Engineering, Data Architecture, or Enterprise Software Development.

- Minimum 4+ years of hands-on experience architecting production-grade solutions natively inside Snowflake and Databricks.

- Snowflake Ecosystem: Proficient in Snowpipe, Streams & Tasks, Time Travel/Fail-safe, Zero-Copy Cloning, Secure Data Sharing, and optimizing multi-cluster warehouses. Experience with Snowpark (Python/Scala) is highly preferred.

- Databricks Ecosystem: Deep understanding of Delta Lake, Databricks Unity Catalog for governance, Delta Live Tables (DLT), Photon engine, and Apache Spark optimization (PySpark/Scala).

- Data Modeling: Masterly command over Data Vault 2.0, Dimensional Modeling (Star/Snowflake schema), and ODS architectural designs.

- Languages: Expert proficiency in Advanced SQL and Python or Scala.

- Tooling: Experience with tools like dbt (data build tool), Airflow, ADF, or native Databricks Workflows.

- Cloud & Infrastructure: Proven expertise in at least one major cloud platform (AWS, Azure, or GCP) along with Infrastructure as Code (Terraform).

- Streaming & Real-Time: Familiarity with Kafka, Confluent, or Spark Structured Streaming for real-time ingestion.

Preferred Certifications:

- Snowflake Certified Advanced Architect

- Databricks Certified Solutions Architect / Data Engineer Professional

- Cloud Architecture Certifications (AWS Solutions Architect, Azure Solutions Architect, etc.)

(ref:hirist.tech)