Data Architect - MDM

Job Title: Associate Data Architect Master Data Management (MDM) Location: Pune - Hybrid Experience: 10+ years of experience in Data Architecture, Data Engineering/Integration with strong exposure into Data Modelling and Database (RDBMS) Management. About the Role We are seeking an Associate Data/Database Architect to join our core product architecture team building an enterprise-grade, multi-domain Master Data Management (MDM) product platform. You will play a key role in optimizing and extending the MDM data model, implementing efficient data ingestion and entity resolution mechanisms, and ensuring the system supports multiple domains such as Party (Individual/Organization), Product, Location, Policy, and Relationship in a cloud-native and scalable manner. Key Responsibilities Data Modeling & Architecture Enhance and extend the existing Party-based data model into a multi-domain MDM schema (Party, Product, Location, Relationship, Policy, etc.). Design and maintain canonical data models and staging-to-core mappings for multiple source systems. Implement auditability, lineage, and soft-delete frameworks within the MDM data model. Contribute to the creation of golden records, trust scores, match/merge logic, and data survivorship rules. Ensure the model supports real-time and batch data mastering across multiple domains. Data Engineering & Integration Help support to optimize data ingestion and ETL/ELT pipeline using Python, PySpark, SQL, and/or Informatica (or equivalent tools). Design and implement data validation, profiling, and quality checks to ensure consistent master data. Work on data harmonization, schema mapping, and standardization across multiple source systems. Help build efficient ETL mappings from canonical staging layers to MDM core data models in PostgreSQL. Develop REST APIs or streaming pipelines (Kafka/Spark) for real-time data processing and entity resolution. Cloud & Platform Engineering Implement and optimize data pipelines on AWS or Azure using native services (e.g., AWS Glue, Lambda, S3, Redshift, Azure Data Factory, Synapse, Data Lake). Deploy and manage data pipelines and databases following cloud-native, cost-effective, and scalable design principles. Collaborate with DevOps teams for CI/CD, infrastructure-as-code, data pipeline and database deployment/migration automation. Governance, Security & Compliance Implement data lineage, versioning, and stewardship processes. Ensure compliance with data privacy and security standards (GDPR, HIPAA, etc.). Partner with Data Governance teams to define data ownership, data standards, and stewardship workflows. Requirements Technical Skills Required Core Skills Data Modelling: Expert-level in Relational (3NF) and Dimensional (Star/Snowflake) modelling; hands-on in Party data model, multi-domain MDM, and canonical models. Database: PostgreSQL (preferred), or any enterprise RDBMS. ER Modelling Tool Erwin/ERStudio, Database Markup Language (DBML). ETL / Data Integration: Informatica, Python, PySpark, SQL, or similar tools. Cloud Platforms: AWS or Azure. Programming: Advanced SQL, Python, PySpark, and/or UNIX/Linux scripting. Data Quality & Governance: Familiarity with data quality rules, profiling, match/merge, and entity resolution. DevOps - Version Control & CI/CD: Git, Azure DevOps, Jenkins, Terraform, Redgate Flyway (preferred) Database Design & Optimization (PostgreSQL) Design and maintain normalized and denormalized models using advanced features (schemas, partitions, views, CTEs, JSONB, arrays). Build and optimize complex SQL queries, materialized views, and data marts for performance and scalability. Tune RDBMS (PostgreSQL) performance indexes, query plans, vacuum/analyze, statistics, parallelism, and connection management. Leverage RDBMS (PostgreSQL) extensions such as: pg_trgm for fuzzy matching and probabilistic search. fuzzystrmatch, pg_vector for semantic similarity and name matching. hstore, jsonb for flexible attribute storage. Implement RBAC, row-level security, partitioning, and logical replication for scalable MDM deployment. Work with stored procedures, functions, and triggers for data quality checks and lineage automation. Implement HA/DR, backup/restore, database-level encryption (at-rest, in-transit), column-level encryption for PII/PHI data. Good to Have Knowledge of Master Data Management (MDM) - Customer, Product etc. Experience with graph databases like Neo4j for relationship and lineage tracking. Knowledge of probabilistic and deterministic matching, ML-based entity resolution, or AI-driven data mastering. Experience in data cataloging, data lineage tools, or metadata management platforms. Familiarity with data security frameworks and Well-Architected Framework principles. Soft Skills Strong analytical, conceptual and problem-solving skills. Ability to collaborate in a cross-functional, agile environment. Excellent communication and documentation skills. Self-driven, proactive, and capable of working with minimal supervision. Strong desire to innovate and build scalable, reusable data frameworks. Education Bachelors or masters degree in computer science, Information Technology, or related discipline. Certifications in AWS/Azure, Informatica, or Data Architecture are a plus. Benefits Why Join Us Be part of a cutting-edge MDM product initiative blending data architecture, engineering, AI/ML, and cloud-native design. Opportunity to shape the next-generation data mastering framework for multiple industry domains. Gain deep exposure to data mastering, lineage, probabilistic search, and graph-based relationship management. Competitive compensation, flexible working and a technology-driven culture.

6- 8 years
Requirements · Proficiency in Python programming. · Advanced knowledge in mathematics and algorithm development. · Experience in developing machine learning and deep learning models. · Strong understanding of neural network architectures, with emphasis on GenAI and LLMs. · Skilled in data processing and visualization. · Experienced in natural language processing. · Knowledgeable in AI/ML deployment, DevOps practices, and cloud services. · In-depth understanding of AI security principles and practices.