: Data Architect

Role Overview

As a Senior Data Architect, you will be responsible for defining, driving, and executing the end-to-end data architecture and technical vision across complex enterprise solutions, projects, and transformation programs.

In this role, you will ensure organizational data is structured, integrated, governed, secured, and optimized to power business applications, advanced analytics, real-time reporting, and cutting-edge AI/ML systems. You will act as the key bridge between business stakeholders and technical execution teamsworking closely with Solution Architects, Software Engineers, DevOps, Security, Analytics, and Business Leaders.

Key Responsibilities

1. Enterprise Data Architecture & Strategy

- Design and execute end-to-end data architectures aligned with strategic business goals and complex solution requirements.
- Develop scalable data models (conceptual, logical, physical), data flows, schemas, and integration blueprints.
- Formulate robust data storage and persistence strategies across transactional (OLTP), analytical (OLAP), operational, and reporting workloads.
- Evaluate and select cloud-native, relational, NoSQL, distributed, and hybrid data platform technologies to ensure maximum performance, scalability, and maintainability.

2. Data Pipeline & Integration Engineering

- Design robust, fault-tolerant integration patterns—including RESTful APIs, ETL/ELT pipelines, event-driven architectures, real-time streaming, and batch processing.
- Define standards for data movement, transformation, synchronization, consistency, and dependency management.
- Architect seamless integration bridges connecting contemporary cloud platforms with legacy enterprise systems.

3. AI & Advanced Analytics Architecture

- Assess data readiness, quality, and structure to support enterprise AI/ML initiatives and advanced analytics.
- Build modern AI data foundation architectures incorporating vector databases, Retrieval-Augmented Generation (RAG) pipelines, semantic search,



and unstructured data ingestion patterns.
- Establish lineage, governance, and ethical safeguards around enterprise data used for training and running AI systems.

4. Governance, Security & Quality Management

- Establish technical controls, privacy policies, and data governance frameworks aligned with regulatory requirements.
- Define access controls, encryption, retention policies, and security mechanisms for sensitive and regulated data.
- Drive automated data quality validation frameworks ensuring data accuracy, completeness, timeliness, and end-to-end lineage tracking.

5. Technical Leadership & Execution Delivery

- Own the technical lifecycle of data solutions from early architecture through implementation and production deployment.
- Provide active technical oversight to engineering teams, conducting code and architecture reviews for data pipelines, queries, and integration solutions.
- Diagnose complex performance bottlenecks, resolve high-severity data issues, and optimize production query/workload efficiency.

6. Presales & Strategic Solutioning

- Partner with growth teams to deliver data architecture inputs for client RFPs, proposals, and solution blueprints.
- Conduct technical feasibility studies, data migration evaluations, sizing estimation, and risk assessments for new projects.
- Lead high-impact client workshops, technical presentations, and architecture alignment sessions.

Key Technical Competencies

- Data Modeling & Architecture: Conceptual, logical, and physical data modeling; Dimensional modeling; Data Lakes, Data Warehouses, and Data Lakehouses.
- Database & Cloud Platforms:



Deep familiarity with modern cloud data platforms (AWS, Azure, GCP, Snowflake, Databricks), Relational (PostgreSQL, MySQL), and NoSQL (MongoDB, Cassandra, DynamoDB) systems.
- Integration & Streaming: ETL/ELT pipeline tools, event-driven architectures (Kafka, RabbitMQ), streaming, and API management.
- AI & Next-Gen Patterns: Vector databases (Pinecone, Milvus, Qdrant), RAG data pipelines, LLM fine-tuning data pipelines, and semantic frameworks.
- Governance & Security: Data lineage, cataloging (e.g., Collibra, Alation), RBAC/ABAC security, and regulatory compliance (GDPR, HIPAA).
- Performance Optimization: Query tuning, data partition strategies, automated testing, and scalable data pipeline optimization.

Behavioral Skills & Core Mindset

- Strategic Business Alignment: Ability to bridge technical data architectures with tangible business outcomes and strategic priorities.
- Cross-Functional Leadership: Exceptional communication and collaboration skills to align engineering, security, analytics, and non-technical business stakeholders.
- Problem-Solving & Mentorship: Strong analytical capability to resolve ambiguous technical hurdles and guide engineering teams toward execution excellence.
- Technical Communication: Skill in simplifying and presenting complex data concepts to C-suite leaders and non-technical audiences.

Qualifications & Experience

- Education: Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, Data Science, or a related quantitative field.
- Experience: 10–15+ years of total experience in software/data engineering, with a strong focus on enterprise data architecture.
- Cloud & Modern Stack: Proven hands-on track record building enterprise data architectures on cloud platforms (AWS, Azure, GCP, Databricks, Snowflake).
- Transformation Impact: Demonstrated success in leading enterprise data modernizations, analytics platform builds, or AI-ready data engineering initiatives preferred.

📌 Data Architect (Mumbai)
🏢 Learningmate Solutions
📍 Mumbai