• Design, build, and optimize high-volume, high-performance ELT pipelines for centralized data warehousing • Collaborate with Product Managers, ML Engineers, Data Scientists, and DevOps to define and enforce a reliable, scalable, and secure data platform architecture • Ensure adherence to data warehousing standards, data quality best practices, and metadata management processes • Take ownership of key data warehouse components and drive improvements in performance and reliability • Conduct architecture reviews and participate in system design discussions to provide technical leadership • Mentor and guide junior engineers, fostering a culture of quality and continuous learning

• Design and develop scalable, reliable data platforms and pipelines for analytics and operations • Implement data processing workflows using distributed frameworks (Apache Spark/pySpark, Databricks) • Evolve the organization’s cloud data platform leveraging Azure technologies (Data Factory, Synapse, Event Hub, Azure Data Lake Storage) • Model data to support analytics, reporting, and downstream consumption • Integrate and process data from multiple sources ensuring quality and consistency • Monitor and optimize pipeline performance, ensuring reliability and scalability • Collaborate with analysts, scientists, and engineers to translate requirements into solutions • Improve data engineering standards, testing, and operational reliability • Evaluate and introduce new tools and technologies in the data engineering ecosystem

• Design, build, and optimize scalable data pipelines in Apache NiFi to automate ingestion, cleansing, and enrichment. • Architect and manage datasets on HDFS for high-volume storage. • Develop distributed processing workflows in PySpark and Hive. • Maintain and tune Postgres databases for high availability and performance. • Partner with Data Scientists to deliver clean datasets for model training. • Implement monitoring and alerting frameworks for pipeline health.

• Build, maintain, and optimize scalable data solutions to support Journey Analytics initiatives. • Maintain and refactor existing codebases, developing modular components. • Ensure high-quality, performant datasets for analytics and reporting use cases. • Maintain, optimize, and automate existing code repositories in GitHub. • Refactor legacy code to simplify maintenance, updates, and reuse across multiple use cases. • Design and build modular, reusable code components to support multiple journeys and reduce duplication. • Develop and manage automated data pipelines in Databricks to support Journey Analytics datasets and downstream reporting. • Consolidate key KPIs, metrics, and attributes into standardized data structures to enable flexible journey views. • Collaborate with analytics and engineering teams to improve data processes and architecture.