• Design, develop, and maintain scalable data models and transformation pipelines using dbt and Snowflake. • Build and manage end-to-end data workflows, from raw ingestion to curated analytical layers. • Develop reusable, modular, and scalable transformations using Jinja and dbt macros. • Select and implement appropriate materializations based on performance and business needs. • Define and implement robust data testing strategies (generic and custom tests). • Ensure high data quality and reliability using tools such as dbt-expectations, dbt-utils, Elementary. • Collaborate with cross-functional teams to understand requirements and deliver data solutions. • Troubleshoot and resolve data quality, performance, and transformation issues. • Contribute to best practices in data modeling, version control, and CI/CD pipelines.

• Design, develop, and maintain scalable data models and transformation pipelines using dbt and Snowflake • Build and manage end-to-end data workflows, from raw ingestion to curated analytical layers • Develop reusable, modular, and scalable transformations using Jinja and dbt macros • Select and implement appropriate materializations (incremental, snapshot, table, view, ephemeral) • Define and implement robust data testing strategies (generic and custom tests) • Ensure high data quality and reliability using tools such as dbt-expectations, dbt-utils, Elementary • Collaborate with cross-functional teams (Data Analysts, Data Scientists, Engineers) to understand requirements and deliver data solutions • Troubleshoot and resolve data quality, performance, and transformation issues • Contribute to best practices in data modeling, version control, and CI/CD pipelines

• Collaborate with stakeholders and source data system teams to understand data requirements • Architect and implement scalable workspace, data lake, dimensional models, data pipelines, data warehouses and other ETL/ELT processes using Fabric. • Work with Fabric assets, Power BI, and other services to build end-to-end data solutions • Ensure data quality, security, and compliance with regulations by implementing data validation, logging, monitoring, and role-based access controls. • Perform root cause analysis on internal/external data and processes to answer specific business questions and identify opportunities for improvement. • Manage platform cost optimization, data quality/governance, and performance tuning • Follow software quality process and methodology standards, including those for design, data quality, code, version control, defect/change request tracking, documentation, work product review, unit testing and environment management. • Review requirements / user stories and provide feedback to the team. Includes participation/input to the requirements process • Integrate AI/ML models and GenAI capabilities into data products and workflows • Help the QA and functional team to identify and define testing strategies for existing and new features • Ability to ensure that solutions developed by development teams fit the business needs • Able to work under pressure and meet deadlines • Comfortable working in evening hours (2pm to 11pm IST)

• Design and build a scalable, org-wide data architecture that serves as the backbone for all current and future products. • Design, build, and maintain data pipelines and ETL/ELT processes across Azure SQL Server, PostgreSQL, Elasticsearch, and third-party sources (Salesforce, APIs). • Evaluate and support a potential migration from MSSQL to PostgreSQL, including feasibility analysis, schema translation, deadlock remediation, performance benchmarking, and migration planning. • Build and maintain a centralized data warehouse or data platform to support analytics, reporting, AI/ML workflows, and our broader data-as-a-service. • Architect metadata models, taxonomies, and tagging systems that enable content enrichment across products (e.g., tagging content with vendors, team size, revenue, industry). • Collaborate with engineering, product, and AI teams to define data models and ensure clean, consistent data flows across systems. • Implement data quality checks, monitoring, and alerting to maintain data integrity across all data products. • Document data architecture, lineage, and dictionary standards for the engineering team. • Support and improve existing Azure SQL databases, Redis caching layers, and Elasticsearch clusters. • Identify and resolve data bottlenecks, query performance issues, and infrastructure gaps.