• Lead a team of data engineers and engineering managers building large-scale, real-time data pipelines in Spark • Act as the primary technical liaison to business leadership, translating trade-offs between technology and business priorities in both directions • Set the multi-year technical direction for Data Engineering and communicate that strategy across both technical and business audiences • Partner with data scientists, analysts, and business stakeholders to turn business requirements into data products the team builds against • Represent Data Engineering in cross-functional and executive planning, building alignment across engineering, product, and business leaders • Set the architectural direction and standards for how data moves across platforms, from streaming ingestion through ETL to aggregation and analytics, hold your managers and teams accountable to that direction, and establish the engineering and agile practices that support delivery speed and quality • Stay close to the technical details to guide architecture design decisions, performance tuning, and tradeoff calls, and to give your team the context they need to move quickly without waiting on you • Work with your teams and the RV Data Platform team to design scalable solutions across distributed systems using AWS, Databricks, and modern big data technologies • Champion a governance-first mindset across your team, making sure the way we collect, move, and use data protects the privacy of the people behind it • Manage and develop the data engineering managers on your team, setting the bar for how they coach, hire, and run their teams • Own career development across your org, building growth paths for individual contributors and managers alike, and coaching through regular, direct feedback • Hold the team accountable for delivering high-quality, scalable solutions across the full data lifecycle

• Own the end-to-end architecture of modern, cloud-native data platforms • Design scalable data ecosystems using **data mesh, data products, and data contracts** • Make high-impact architectural decisions across ingestion, storage, processing, and access layers • Ensure platforms are secure, compliant, and production-grade by design • Design and deliver cloud-native data platforms using **Databricks, Snowflake, AWS, and GCP** • Apply modern architectural patterns: **data mesh, data products, and data contracts** • Integrate deeply with client systems to enable scalable, consumer-oriented data access • Build and optimise **batch and real-time pipelines** • Work with streaming and event-driven tech such as **Kafka, Flink, Kinesis, Pub/Sub** • Orchestrate workflows using **Airflow, Dataflow, Glue** • Process and transform large datasets using **Spark and Flink** • Design systems that perform in production - not just on paper • Work across relational, NoSQL, and analytical stores (Postgres, BigQuery, Snowflake, Cassandra, MongoDB) • Optimise storage formats and access patterns (Parquet, Delta, ORC, Avro) • Implement secure, compliant data solutions with **security by design** • Embed governance without killing developer velocity • Work directly with clients to understand problems and shape solutions • Translate business needs into pragmatic engineering decisions • Act as a trusted technical advisor, not just an order taker • Set engineering standards, patterns, and best practices across teams • Review designs and code, providing clear technical direction and mentorship • Raise the bar on data quality, testing, observability, and operational excellence

• Analyze existing data import processes in an SQL-based data warehouse. • Design an automated import tool to support and automate ETL processes. • Develop a concept for automatic detection of data structures and data types, optionally using AI-based methods. • Conduct a market and tool analysis of existing solutions for data integration and provisioning as part of a make-or-buy assessment. • Evaluate the developed concept in terms of degree of automation, maintainability, and extensibility. • Document and present the results of your bachelor's thesis.

• Diseñar, desarrollar y mantener canalizaciones de datos (ETL/ELT) utilizando Python y SQL, bajo la Arquitectura Medallion, asegurando la transformación, limpieza y estructuración de los datos en Google Cloud Platform (GCP). • Diseñar modelos de datos relacionados y optimizar las consultas SQL para garantizar un procesamiento eficiente, escalable y de alto rendimiento, optimizando el uso de la infraestructura. • Desarrollar y mantener paneles e indicadores en Looker Studio y Grafana, facilitando la visualización de métricas clave para equipos de Producto, Negocio y Análisis. • Implementar prácticas de gobernanza, seguridad y automatización de datos a través de herramientas DevOps, asegurando el cumplimiento de los estándares de seguridad de la información e impulsando el uso de API e ingeniería de indicaciones para optimizar procesos.