Data Architect

Role & responsibilities

Job Title: Data Architect

Experience: 1016 years

Role Overview:

The Data Architect will be responsible for designing, governing, and optimizing enterprise data architecture to enable scalable analytics, AI, and digital transformation initiatives.

This role involves defining the data strategy, integration patterns, governance models, and architecture blueprints across data ingestion, storage, processing, analytics, and AI enablement layers — ensuring alignment with business goals, performance, and compliance.

The ideal candidate combines deep technical expertise with strategic thinking, balancing data engineering, modeling, cloud platform design, and governance capabilities.

Key Responsibilities:

1. Data Architecture & Design

• Define the enterprise data architecture blueprint covering ingestion, transformation, storage, and consumption layers.
• Design and govern data models, data lakes, warehouses, and semantic layers for analytics and AI workloads.
• Architect batch, streaming, and real-time data processing pipelines.
• Ensure scalability, reusability, and maintainability of data components across projects.

2. Platform & Integration

• Architect and implement data platforms on Azure, AWS, or GCP — leveraging services like Microsoft Fabric, Synapse, Databricks, Data Lake, or Snowflake.
• Define and oversee ETL/ELT frameworks using ADF, Fabric Data Factory, or similar orchestration tools.
• Design integration architectures to connect enterprise systems (ERP, CRM, IoT, APIs) with centralized data platforms.
• Collaborate with application teams to embed data services and APIs into operational and analytical solutions.

3. Governance, Security & Compliance

• Establish data governance, metadata management, and data cataloging practices (using Microsoft Purview or equivalent).
• Define policies for data quality, lineage, retention, and access control.
• Ensure compliance with data privacy and protection standards (GDPR, SOC2, ISO
• Partner with InfoSec and Compliance teams to secure sensitive data and enable controlled data sharing.

4. Advanced Analytics & AI Readiness

• Enable AI/ML and GenAI workloads by architecting data pipelines for model training and inference.
• Design RAG (Retrieval-Augmented Generation) and vector database integration architectures for GenAI solutions.
• Work with Data Scientists and AI Engineers to ensure data availability, quality, and performance for analytical models.
• Enable Microsoft Fabric, Azure OpenAI, and Copilot integrations for data-driven insights.

5. Strategy, Governance & Collaboration

• Define data architecture principles, frameworks, and standards across the organization.
• Support the establishment of a Data Architecture Council or Center of Excellence (CoE).
• Collaborate with Business, Cloud, and AI Architects to align data initiatives with enterprise architecture goals.
• Mentor data engineers and analysts to adopt best practices in design, performance tuning, and governance.

Required Skills & Experience:

• 10–16 years of experience in data architecture, data engineering, or analytics platform design.
• Deep expertise in cloud data platforms — Azure (Fabric, Synapse, Data Lake, Databricks) preferred; AWS/GCP experience a plus.
• Strong proficiency in data modeling (conceptual, logical, physical) and database technologies (SQL Server, Snowflake, PostgreSQL, Cosmos DB).
• Proven experience with ETL/ELT tools, data pipelines, and metadata management.
• Proficiency in SQL, Python, and Spark for data processing and automation.
• Understanding of modern data architectures — Data Mesh, Data Lakehouse, and Fabric.
• Experience implementing data governance and data lineage frameworks (Purview, Collibra, or Alation).
• Familiarity with Power BI, semantic modeling, and performance tuning.
• Strong understanding of security, compliance, and cost optimization in cloud environments.

Good-to-Have Skills:

• Experience with Generative AI, LLM data preparation, and RAG-based AI architectures.
• Knowledge of Microsoft Fabric, Copilot integration, or AI-driven analytics.
• Familiarity with API design, microservices, and event-driven data architectures (Kafka, Event Hub).
• Exposure to DataOps and MLOps practices.

• Certifications:

• Microsoft Certified: Azure Data Engineer / Fabric Analytics Engineer / Solution Architect Expert

• AWS Certified Data Analytics – Specialty

Education:

• Bachelor's or Master's degree in Computer Science, Data Engineering, or Information Systems.

Soft Skills:

• Strategic mindset with the ability to align data architecture to business outcomes.
• Excellent communication and documentation skills.
• Strong collaboration and stakeholder management abilities.
• Passion for data innovation, automation, and continuous improvement.