Data Architect

About the Position

We are seeking a Data Architect to join our AI project team in Noida, India. In this role, you will design and implement the data architecture needed to support machine learning and AI solutions, including defining data models, storage patterns, and governance frameworks. You will ensure that data from various sources is well-organised, accessible, and AI-ready, working closely with data engineers and ML engineers to build robust data pipelines and maintain high data quality for analytics and model development.

Responsibilities

• Data Modelling & Schema Design: Develop and maintain data models (conceptual, logical, and physical) that define how data is stored and related. This includes designing relational schemas, graph data models for knowledge graphs, and time-series data structures as needed, ensuring they accurately represent business entities and relationships.
• Data Storage Architecture: Define and implement data storage and management patterns that optimise data retrieval and analytics performance. This involves selecting or designing appropriate storage solutions (e.g. relational databases, NoSQL/graph databases, data warehouses, data lakes) and structuring them for scalability and fast access to large datasets used in AI projects.
• Data Pipelines & Integration: Build and oversee robust data pipelines (ETL/ELT processes) to integrate data from multiple sources into centralised platforms. You will design workflows to collect, transform, and load data into analytics repositories or feature stores, guaranteeing that AI models have consistent, well-prepared data to work with.
• Data Governance & Quality: Establish and enforce data governance policies and standards. This means defining practices for data quality, data cleaning, and master data management, as well as setting security and privacy controls to protect sensitive information.
• Metadata Management & Lineage: Implement frameworks for data metadata management and lineage tracking. This includes maintaining data catalogues or dictionaries that describe data meaning (possibly leveraging ontologies), and tools or processes to trace how data flows through pipelines and transformations.
• Collaboration with Engineering Teams: Work closely with data engineers, ML engineers, and data scientists to ensure the data architecture meets their needs. You will collaborate on designing data interfaces (e.g. APIs or query endpoints) and assist in shaping how data is used for features in machine learning.
• Performance Optimisation & Scaling: Monitor the performance and scalability of the data infrastructure, and tune it as the AI project grows. Optimise database queries, indexing, and storage layouts for faster model training and inference data access.

Requirements

Must Have

• Education: Bachelor's degree in Computer Science, Information Systems, or a related field (or equivalent professional experience). An advanced degree is a plus but not required.
• Experience: Approximately 3-5 years of experience in data architecture, data engineering, or a related data management role. A proven track record in designing data solutions and managing data schemas is expected.
• Data Modelling & Databases: Strong proficiency in data modelling and database design. You should be comfortable creating ER diagrams and defining relational schema, as well as working with NoSQL databases (e.g. document or graph databases). Practical experience with SQL and at least one relational database is required, as well as deep knowledge of other data store types (especially graph databases) is highly needed.
• Data Pipeline Development: Hands-on experience developing data pipelines and integration workflows. This includes proficiency in ETL/ELT tools or frameworks (or custom scripting with Python/SQL) to gather and transform data. You should understand how to optimise data flow and have experience with batch processing; experience with real-time streaming data (e.g. using Kafka or equivalent) is a plus.
• Cloud Data Platforms: Experience working with cloud-based data platforms or big data technologies. While our approach is cloud-agnostic, you should be familiar with concepts like data lakes, data warehouses, and distributed computing in a cloud environment (e.g. using AWS, Azure, or GCP services).
• Data Governance & Security: Solid understanding of data governance principles and best practices. You should be knowledgeable about data privacy regulations and data protection techniques, ensuring compliance in how data is stored and used.
• Communication & Teamwork: Excellent communication skills with the ability to collaborate in cross-functional teams. You should be able to translate complex data architecture concepts into clear terms for project managers or stakeholders, and work closely with engineering teams to guide implementation.

Nice to Have

• Ontologies & Knowledge Graphs: Exposure to semantic data modelling, ontologies, or knowledge graph construction. Experience in structuring data with ontologies (e.g. using RDF/OWL standards) or implementing a knowledge graph to link datasets can be very beneficial.
• AI/ML Project Involvement: Experience working on projects that involve AI or machine learning, where you partnered with data scientists or ML engineers.
• Data Governance Tools: Familiarity with data governance or data cataloguing tools (such as Collibra, Alation, or Apache Atlas) and lineage-tracking systems.
• Modern Data Architecture Patterns: Experience with modern data architecture concepts and patterns.
• Certifications: Relevant industry certifications are advantageous.

We Offer

• Competitive salary
• Opportunity to work in a dynamic and innovative environment
• Professional growth and development opportunities

About the Company

Join our team and contribute to cutting-edge AI projects that are shaping the future of technology.