• Build scalable data pipelines using Python, Spark and Airflow to move data from different applications into our data lake. • Collaborate and influence the long term technology strategy and innovations roadmaps across data engineering areas. • Provide technical leadership, mentorship and growth to junior team members as we scale • Collaborate closely with engineering and product to elevate data generation approaches • Manage the end-to-end life cycle on the development of new features and pipelines • Ideate and contribute to shared data engineering tooling and standards. • Define and promote data engineering best practices across the company.

• Design, implement, and validate ETL/ELT data pipelines–for batch processing, streaming integrations, and data warehousing, while maintaining comprehensive documentation and testing to ensure reliability and accuracy. • Maintain end-to-end Snowflake data warehouse deployments and develop Denodo data virtualization solutions. • Recommend process improvements to increase efficiency and reliability in ELT/ETL development. • Stay current on emerging data technologies and support pilot projects, ensuring the platform scales seamlessly with growing data volumes. • Architect, implement and maintain scalable data pipelines that ingest, transform, and deliver data into real-time data warehouse platforms, ensuring data integrity and pipeline reliability. • Partner with data stakeholders to gather requirements for language-model initiatives and translate into scalable solutions. • Create and maintain comprehensive documentation for all data processes, workflows and model deployment routines. • Should be willing to stay informed and learn emerging methodologies in data engineering, and open source technologies.

• Perform data modeling by analyzing and structuring datasets to ensure proper relationships and adherence to industry best practices. • Handle special data by creating and managing equivalence tables, including specific cases such as “verticals”, aiming for greater standardization and efficiency. • Build pipelines that ensure full traceability from client file delivery through to loading into the Gold layer in Azure Databricks. • Identify opportunities for source-side automation to reduce reliance on manual files and increase efficiency. • Propose and implement efficient migration strategies, such as incremental loads, to avoid high costs from daily full loads. • Familiarity with code documentation. • Experience with agile methodologies. • Familiarity with developing process monitoring and alerts. • Ability to communicate technical solutions in a simple, pragmatic manner. • Proactive in supporting the team.

• Organization and Structuring of Data Repositories • Perform data modeling, analyzing and structuring datasets to ensure proper relationships and adherence to market best practices • Handle special data cases by creating and managing equivalence/mapping tables, including specific cases such as “verticals,” aiming for greater standardization and efficiency • Development of Data Pipelines and Flows • Build pipelines that ensure full traceability, from client file delivery through to loading into the Gold layer on Google Cloud Platform (GCP) • Identify opportunities for source-side automation to reduce dependence on manual files and increase efficiency • Migration and Optimization of Databases • Propose and implement efficient migration strategies, such as incremental loads, avoiding high costs associated with daily full loads • Familiarity with code documentation (desirable) • Experience with agile methodologies (desirable) • Skills in developing and optimizing APIs for data consumption (desirable) • Familiarity with developing monitoring and alerts for processes implemented on GCP (desirable) • Ability to communicate technical solutions in a simple and pragmatic way (soft skill) • Proactive in supporting the team (soft skill)