• Build infrastructure that enables analytics and data science teams to deliver innovative, impactful solutions for clients. • Assist clients in migrating their existing business intelligence and data warehouse solutions to Google Cloud. • Design, develop, and optimize robust data pipelines, making data easily accessible for visualization and machine learning applications. • Design and implement new data warehouse and data mart solutions including transforming, testing, deploying, and documenting data. • Architect, maintain, and troubleshoot cloud-based infrastructure to ensure high availability and performance. • Work closely with technology partners such as Google Cloud, Snowflake, dbt, and Looker, mastering their technologies and building a network with their engineers. • Collaborate in an agile and dynamic environment with a team of data engineers, BI analysts, data scientists, and machine learning experts. • Implement software engineering best practices to analytics processes, such as version control, testing, and continuous integration.

• Design, implement, and maintain data ingestion, processing, and storage solutions using Azure platform tools such as Azure Databricks, Azure Data Factory, Azure Data Lake, and SQL databases; • Develop and optimize ETL (Extract, Transform, Load) workflows using PySpark to ensure data integrity and quality; • Model and structure data efficiently to meet the organization's analytics and BI requirements; • Monitor and troubleshoot data infrastructure issues, ensuring system availability and scalability.

• Design and implement scalable, high-throughput data ingestion systems that integrate internal and external data across domains • Design and build core data platform components, including ingestion, validation, orchestration, and lineage, with a focus on code quality and reliability • Build and evolve a centralized data lake using Apache Iceberg (or similar table formats) • Work across multi-cloud environments (AWS, GCP, Azure) to design and implement cloud-agnostic data ingestion and processing patterns • Contribute hands-on to the Semantic layer, ensuring data is easy to consume for BI and analytics teams • Partner with Senior Data Engineers, Platform Engineers, and Analytics Engineers to align how data is produced, stored, and consumed • Establish practical engineering standards for testing, observability, and operational excellence • Provide technical leadership through mentorship, code reviews, and design discussions, while remaining hands-on

• Develop and maintain scalable ETL/ELT data pipelines, performing extraction, transformation, and loading of data from multiple sources, using Pentaho Data Integration as the primary platform. • Integrate new data sources into existing systems, ensuring data quality, consistency, and availability for analytics and business teams (for example, for Power BI dashboards). • Optimize and monitor the performance of data processes, performing tuning and adjustments as needed to ensure efficient, reliable, and low-latency pipelines. • Collaborate with the Automation Architect and BI/Analytics teams to understand data requirements for automation and reporting projects, and deliver solutions aligned to those needs. • Implement data governance, security, and compliance practices, ensuring pipelines adhere to data protection policies and that data quality is continuously verified (monitoring for failures, missing data, etc.). • Document developed data processes and flows, keeping clear records of transformations, pipeline configurations, and architecture to facilitate maintenance and future evolution.