Data Architect

Overview

Job Purpose

Senior Data Architect

• to lead the design and evolution of enterprise-scale modern data platforms across cloud and AI ecosystems.
• This role is ideal for architects who are passionate about:
• Azure-native lakehouse platforms
• Data Mesh architecture
• Big Data and real-time streaming
• Enterprise data governance
• GenAI-ready data foundations
• Building scalable, secure, cloud-native analytics platforms
• You will work closely with engineering teams, business stakeholders, data governance offices, and AI teams to architect next-generation enterprise data ecosystems supporting analytics, AI/ML, and operational intelligence.

Modern Data Platform Architecture

• Design and implement enterprise-scale
• Lakehouse architectures
• using Azure Databricks, ADLS Gen2, Delta Lake, and Synapse.
• Define scalable patterns for batch, streaming, and near real-time data ingestion.
• Architect domain-driven

Data Mesh

• platforms with reusable data products and federated governance.
• Build highly scalable distributed data systems capable of handling structured and unstructured enterprise data.

Big Data & Streaming

• Design and optimize large-scale distributed data processing systems using:
• Apache Spark

• Delta Lake And Iceberg

• Databricks
• Flink (preferred)
• Implement scalable CDC and event-driven architectures.
• Define enterprise ingestion standards for APIs, databases, ERP systems, SaaS platforms, and streaming systems.

Data Governance & Enterprise Standards

• Establish enterprise-wide governance standards covering:
• Metadata management
• Data lineage
• Data quality
• Security classification

• Master Data Management (MDM)

• Business glossary
• Implement governance tooling such as:
• Unity Catalog
• Microsoft Purview
• Collibra
• Ataccama
• Alation
• Define enterprise data modeling standards and architecture principles.

Ai & Genai Enablement

• Architect AI-ready data platforms supporting:
• ML workloads
• Feature stores
• Vector databases
• RAG pipelines
• GenAI use cases
• Collaborate with Data Science and AI teams to enable scalable AI data foundations.
• Drive adoption of modern AI data patterns including semantic search and GraphRAG architectures.

Cloud & Platform Engineering

• Architect cloud-native data platforms on Azure.
• Lead platform modernization and migration initiatives from on-prem to cloud.
• Define CI/CD, Infrastructure-as-Code, observability, and FinOps best practices.
• Collaborate with security teams to ensure compliance and enterprise-grade platform security.