• Dive deep into blockchain data (Ethereum and other L1/L2s) and enrich it with insights from various data providers. • Build pipelines and infrastructure to power a variety of analytics and dashboards. • Work across teams, design new features, and take ownership of sourcing, verifying, and maintaining high-quality data.

• Lead and provide expert support for data collection, data validation, data visualization, and analytics initiatives • Apply disciplined methodologies for the planning, analysis, design, and development of information systems on an enterprise-wide basis or across a business sector • Develop analytical techniques and methodologies to solve complex business and technical problems • Perform strategic systems planning, business information planning, and business analysis • Organize and analyze large volumes of structured and unstructured data sets using data analytical tools • Locate, access, merge, clean, and standardize data from multiple sources, and develop derived metrics • Create and implement data collection and analysis tools using programming languages such as Python, Databricks, SQL, Scala, R, and Java • Design, script, debug, and analyze data engineering solutions • Implement and create machine learning-based tools and processes • Apply distributed and parallel processing technologies (e.g., Spark) to handle big data analytics tasks involving large data volumes • Perform SQL Server data imports from CSV and TXT files • Leverage Excel and Google Suite for data analysis, reporting, and collaboration • Utilize supporting tools and platforms such as Pentaho (data import/transformation), Azure Data Studio, GitHub, and Smartsheet as needed • Document task requirements, work completed, processes, and technical details thoroughly • Communicate effectively with stakeholders across technical and business teams • Operate independently as a subject matter expert in a fast-paced, entrepreneurial environment

• 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