Data Architect

BrainWave Professionals; India; On-site; INR 4,000,000 - 6,500,000; Full time

Job summary

BrainWave Professionals is seeking a Senior Data Architect / Lead Data Engineer to define target architectures and lead delivery of modern data platforms. You will work across pipelines, semantic models, and governance while collaborating with enterprise teams to build production‑grade data products.

The role combines deep architectural thinking with hands‑on engineering, supporting analytics, ML, GenAI use cases, and AI‑ready data foundations in a remote, full‑time capacity.

Qualifications

• 10–15+ years of data architecture and engineering experience.
• Strong mix of architectural thinking and hands‑on delivery.
• Experience with modern data platforms and AI-enabled data foundations.

Responsibilities

• Define current, transition, and target‑state architectures.
• Design data platforms including warehouses, lakes, mesh, and fabric.
• Build scalable ingestion, integration, transformation, and activation pipelines.
• Develop data models, semantic layers, and knowledge graphs.
• Establish governance, metadata, lineage, and security.
• Mentor engineers and contribute hands‑on to delivery.

Skills

Data architecture

Cloud data platforms

ETL/ELT

GenAI / AI readiness

Stakeholder management

Mentoring engineers

Architecture reviews

Education

Bachelor's or Master's degree

Tools

Snowflake

Databricks

AWS

Azure

GCP

Job description

Senior Data Architect / Lead Data Engineer

Employment Type:

Full-time, Remote

Role Overview

We are seeking an exceptional Senior Data Architect / Lead Data Engineer who combines deep architectural thinking with strong hands‑on engineering capabilities.

This is not a documentation‑only architecture role. The ideal candidate can define the target architecture, make critical technology decisions, design reusable patterns, and work directly with engineering teams to build and deliver production‑grade solutions.

You will help design and implement modern enterprise data platforms, integration frameworks, governed data products, semantic layers, knowledge graphs, GraphRAG solutions, and AI‑ready data foundations.

You will work closely with clients, enterprise architects, AI engineers, product teams, and delivery leaders while helping shape technical standards, delivery methodologies, reusable assets, and engineering culture.

Key Responsibilities

• Define current, transition, and target‑state architectures.
• Design modern data platforms, including warehouses, lakes, lakehouses, mesh, and fabric architectures.
• Build scalable ingestion, integration, transformation, orchestration, and activation pipelines.
• Define ETL/ELT/API/CDC/streaming integration patterns.
• Design governed data products.
• Develop conceptual, logical, physical, canonical, and semantic data models.
• Build semantic layers, ontologies, knowledge graphs, GraphRAG solutions, and vector stores.
• Establish metadata, lineage, governance, observability, security, and compliance.
• Support analytics, ML, GenAI, and intelligent‑agent use cases.
• Create reference architectures and engineering standards.
• Perform architecture reviews and optimization.
• Partner directly with clients and stakeholders.
• Mentor engineers and contribute hands‑on to delivery.

Required Experience and Skills

• 10–15+ years of experience in data architecture and engineering.
• Strong combination of architecture expertise and hands‑on engineering.
• Deep understanding of the end‑to‑end data lifecycle.
• Experience with ETL, ELT, CDC, APIs, and streaming.
• Experience with cloud and modern data platforms, including Snowflake, Databricks, AWS, Azure, and/or GCP.
• Strong understanding of data warehouses, lakehouses, data mesh, and data fabric architectures.
• Experience with data governance, metadata, lineage, and data quality.
• Experience designing data products and data contracts.
• Ability to build analytics and AI‑ready data foundations.
• Excellent communication and stakeholder‑management skills.
• Ability to explain complex technical concepts clearly.

AI, Semantic, and Context Engineering Experience

• Experience with LLMs, GenAI, and intelligent agents.
• Experience with RAG, GraphRAG, knowledge graphs, and vector databases.
• Knowledge of ontologies and semantic modeling.
• Experience with metadata‑driven automation.
• Understanding of AI evaluation and responsible AI practices.
• Experience with enterprise AI integrations.
• Experience applying AI to data quality and lineage.
• Experience with AI‑assisted engineering tools such as Claude Code, Cursor, GitHub Copilot, Windsurf, OpenAI, and Gemini.
• Familiarity with agentic frameworks.
• Understanding of Model Context Protocol (MCP).
• Experience with AI‑assisted software and data engineering.
• Strong prompt‑engineering and workflow‑automation skills.
• Ability to implement AI workflows with appropriate human validation and governance.

Leadership and Startup Mindset

• Strong startup mindset and entrepreneurial approach.
• Independent execution and ownership.
• Comfortable working hands‑on across architecture and delivery.
• High level of accountability for technical and customer outcomes.
• Willingness to challenge assumptions and explore better approaches.
• Commitment to continuous learning.
• Ability to mentor and develop other engineers.
• Interest in contributing to the growth and evolution of the organization.

Education

• Bachelor’s or Master’s degree in a relevant field.
• Relevant cloud, data, or AI certifications are a plus.

What You’ll Get to Do

• Build modern Data, AI, and Enterprise Architecture solutions.
• Work on high‑value, complex enterprise problems.
• Design and build modern data and AI platforms.
• Influence technical strategy and architecture decisions.
• Work with experienced technical and business leaders.
• Grow into broader technical and leadership responsibilities.

Ideal Candidate

We are looking for builders who combine architecture depth, engineering discipline, curiosity, and a strong commitment to customer outcomes. The ideal candidate is equally comfortable defining an enterprise architecture, making technology decisions, writing production‑grade code, mentoring engineers, and working directly with clients to solve complex problems.