Data Architect

Role & responsibilities

We are seeking an experienced Lead Technical Architect to lead enterprise-scale data platform modernization initiatives, including migration from legacy/on-premise data warehouses, ETL platforms, and cloud data ecosystems to Databricks Lakehouse and dbt-based modern data architectures.

The ideal candidate will possess deep expertise in cloud data platforms, data architecture, data engineering, migration strategies, governance, and modernization programs. This role requires strong leadership skills to define target-state architectures, drive migration roadmaps, mentor engineering teams, and engage with business and executive stakeholders.

Key Responsibilities

Architecture & Strategy

• Define enterprise data modernization strategy and target-state architecture leveraging Databricks Lakehouse and dbt.
• Design scalable, secure, and high-performance data platforms supporting analytics, AI/ML, reporting, and self-service BI.
• Develop migration blueprints for legacy ETL, data warehouses, and reporting platforms.
• Create reference architecture, standards, and governance frameworks

Data Platform Migration

• Lead migration initiatives from platforms such as:
• Teradata, Oracle ,SQL Server ,Netezza ,Hadoop ,Informatica ,Ab Initio ,Talend, Snowflake (optimization/consolidation scenarios)
• Assess source systems and define migration patterns, tooling, and execution strategies.
• Drive workload transformation to Databricks using Delta Lake, Unity Catalog, and Lakehouse architecture.
• Lead ETL/ELT modernization using dbt.

Databricks Architecture

• Design and implementation:
• Delta Lake architecture
• Medallion Architecture (Bronze, Silver, Gold)
• Unity Catalog governance model
• Data Quality frameworks
• Performance optimization strategies
• Cost optimization and FinOps practices
• Establish best practices around Spark, PySpark, SQL, and Databricks Workflows.

dbt Leadership

• Define enterprise dbt standards and deployment strategies.
• Design modular, reusable dbt models and frameworks.
• Implement testing, lineage, documentation, and CI/CD pipelines.
• Establish best practices for semantic layers and data transformation governance.

Cloud & DevOps

• Architect solutions on Azure, AWS, or GCP.
• Implement Infrastructure as Code (Terraform preferred).
• Integrate CI/CD pipelines using Azure DevOps, GitHub Actions, Jenkins, or GitLab.
• Drive DataOps and MLOps practices across teams.

Leadership & Stakeholder Management

• Lead teams of architects, data engineers, and consultants.
• Conduct architecture reviews and technical governance forums.
• Engage business stakeholders to understand transformation goals.
• Provide executive-level presentations, estimates, risk assessments, and roadmaps.

Preferred candidate profile

• SQL (Expert Level), PySpark / Spark, Python, Data Modeling (Dimensional & Data Vault) , ETL/ELT Architecture, Data Warehousing Concepts ,API Integrations