Data Architect

Role & responsibilities

Job Title: Data Architect

Experience: 1016 years

Role Overview:

Data Architect

data engineering, modeling, cloud platform design, and governance

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 27001).
• 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.