• Design and build robust, scalable data transformation pipelines using SQL, DBT, and Jinja templating • Develop and maintain data architecture and standards for Data Integration and Data Warehousing projects using DBT and Amazon Redshift • Monitor and ensure the smooth operation of data pipelines between OneTrust (OT) application and external data platforms (ESPs, CRMs, etc.). • Collaborate with cross-functional teams to gather requirements and deliver dimensional data models that serve as a single source of truth • Assist in the ongoing data integration tasks (e.g., connectors, APIs) between OT and various business systems. • Own the full stack of data modeling in DBT to empower analysts, data scientists, and BI engineers • Enhance and maintain the analytics codebase, including DBT models, SQL scripts, and ERD documentation. • Familiarize regulatory compliance guidelines across different geographical areas (CCPA, GDPR) and integrate them into current and new pipelines for consent collection. • Ensure data quality, governance alignment, and operational readiness of data pipelines • Apply software engineering best practices such as version control, CI/CD, and code reviews • Optimize SQL queries for performance, scalability, and maintainability across large datasets • Implement best practices for SQL performance tuning, including partitioning, clustering, and materialized views • Build and manage infrastructure as code using AWS CDK for scalable and repeatable deployments. Integrate and automate deployment workflows using AWS CodeCommit, CodePipeline, and related DevOps tools • Support Agile development processes and collaborate with offshore teams

• Develop and maintain a comprehensive data architecture and cloud strategy that aligns with the organization's goals and needs. • Design, implement, and manage cloud-based data infrastructure on AWS, ensuring scalability, reliability, and cost-efficiency. • Utilize AWS services (S3, Glue, EMR, Redshift, Lambda, Kinesis, MWAA, etc.) to build and optimize data pipelines and storage solutions. • Champion the use of data lakehouse architecture and optimize its performance for analytical and operational workloads. • Identify the gaps and opportunities in the current system and suggest/implement to optimise the processes and costs. • Lead and guide data engineering teams to develop, maintain, and optimize ETL processes for data ingestion, transformation, and loading. • Implement real-time data processing solutions using technologies such as Apache Kafka and AWS Kinesis. • Collaborate with data scientists, business stakeholders and analysts to ensure data availability and quality, enabling effective analytics and reporting. • Leverage DBT for data modelling and transformation to support self-service analytics and data governance. • Architect and implement data integration solutions for API ingestion, enabling data from diverse sources to be captured, transformed, and ingested into our data lakehouse. • Utilize Airbyte and custom APIs to ensure efficient, reliable, and secure data transfers. • Manage data integration pipelines to support real-time and batch data processing. • Design, configure, and maintain workflow orchestration using Apache Airflow to automate ETL processes and data pipeline executions. • Monitor and optimize job scheduling, error handling, and performance of data workflows. • Implement data security protocols, access controls, and encryption to safeguard sensitive data, especially PIIs. • Ensure compliance with data privacy regulations and industry standards. • Collaborate with cross-functional teams to understand data requirements and provide data solutions to meet their needs. • Maintain comprehensive documentation for data engineering and data architecture processes and solutions. • Guide the team in setting up cloud Infra and automate using tools like terraform, cloud formation, Jenkins etc • Guide the operations team in setting up automated monitoring & alerts mechanism

• Design and build robust, scalable data transformation pipelines using SQL, DBT, and Jinja templating • Develop and maintain data architecture and standards for Data Integration and Data Warehousing projects using DBT and Amazon Redshift • Collaborate with cross-functional teams to gather requirements and deliver dimensional data models that serve as a single source of truth • Own the full stack of data modeling in DBT to empower analysts, data scientists, and BI engineers • Enhance and maintain the analytics codebase, including DBT models, SQL scripts, and ERD documentation • Ensure data quality, governance alignment, and operational readiness of data pipelines • Apply software engineering best practices such as version control, CI/CD, and code reviews • Optimize SQL queries for performance, scalability, and maintainability across large datasets • Implement best practices for SQL performance tuning, including partitioning, clustering, and materialized views • Build and manage infrastructure as code using AWS CDK for scalable and repeatable deployments. Integrate and automate deployment workflows using AWS CodeCommit, CodePipeline, and related DevOps tools • Support Agile development processes and collaborate with offshore teams

• Start your day with a short standup • Heads-down focus time to plan, build, iterate, and launch • Minimal meetings — maximum ownership • Creating an understanding of the creators location, age, and interests at scale • Creating systems to extract collaborations between creators and brands from raw social data • Shaping the future of AI-assisted search, exploring how LLMs and embeddings can enhance search and recommendations.