• Design, build, and maintain scalable ETL/ELT pipelines. • Implement and manage reverse ETL workflows that sync data from the data warehouse (e.g., Snowflake) into operational systems (CRMs, marketing tools, internal applications, etc.). • Optimize data models to support both analytics and activation use cases. • Ensure data quality, validation, and monitoring across pipelines. • Collaborate with cross-functional teams to translate business requirements into reliable data solutions. • Support performance tuning and cost optimization of warehouse workloads. • Maintain documentation and best practices across data workflows.

• Build and maintain ELT pipelines that ingest data from systems. • Co-own the mapping and migration of source data into the new 3NF EDW, ensuring data integrity, reducing redundancy, and maintaining automated unit and data tests. • Develop data observability processes and monitoring dashboards to track pipeline health, freshness, and data quality across Databricks. • Build new data and AI-powered tooling to improve the productivity of the data engineering team and broaden self-service data access for Route employees and external partners. • Help harden the Integration Pipeline by automating deployment of shared staging and production infrastructure for new pipelines and managing dependency updates for dbt and CI templates. • Support the full migration from Snowflake to Databricks, targeting completion by end of Q2 2027, including reporting services and ingest/egress jobs. • Coordinate with engineering, analytics, product, and business teams to define and prioritize data requirements and ensure end-to-end data lifecycle coverage for existing and new products. • Champion data democratization, help establish a company-wide data retention policy, and expand the foundation for a self-service Silver layer (EDW) that serves as a single source of truth.

• Develop standardized practices for delivering new products and capabilities using Big Data & cloud technologies, including data acquisition, transformation, analysis, Modelling, Governance & Data management skills • Interact with senior client technology leaders, understand their business goals, create, propose solution, estimate effort, build architectures, develop and deliver technology solutions • Define and develop client specific best practices around data management within a cloud environment • Recommend design alternatives for data ingestion, processing and provisioning layers • Design and develop data ingestion programs to process large data sets in Batch mode using ADB, ADF, PySpark, Python, Snypase • Develop data ingestion programs to ingest real-time data from LIVE sources using Apache Kafka, Spark Streaming and related technologies • Have managed team and have experience in end to end delivery • Have experience of building technical capability and teams to deliver

• The Product Growth Specialist (PGS) will drive the theater-wide activation of Product Go-to-Market (GTM) initiatives, specifically focused on Data Engineering. • The PGS will act as a critical cross-functional interlock, designed to plug product-strategy gaps and accelerate GTM program execution. • They will ensure faster execution of GTM programs and sales plays. • Build a tight interlock between product teams and field execution, acting as the "Voice of the Field". • Continuously scan customer, market, and pipeline trends to propose new programmatic initiatives for Product Strategy teams. • Provide critical support for high-value deals by ensuring optimal cross-functional orchestration.