Senior Analytics Engineer

BME | Bolsas y Mercados Españoles; Madrid; Presencial; EUR 40.000 - 60.000; Jornada completa

Descripción de la vacante

A leading financial organization in Madrid seeks an Analytics Engineer to develop a self-service ecosystem using tools like Azure and Power BI. The role involves building datasets, curating high-quality analytical components, and supporting users in their analytics needs. Ideal candidates will have strong skills in Azure, Databricks, SQL, Python, and Power BI, along with a degree in a technical field. A passion for teaching and improving user capabilities is essential, as well as experience with data modeling and analytics environments.

Formación

• Strong experience with Azure, Databricks, SQL, Python, and Power BI.
• Solid background in preparing data for analytics using modern patterns.
• Clear communication skills to translate technical concepts for users.
• Experience applying testing, documentation, and version control.
• Practical understanding of tuning analytics environments.

Responsabilidades

• Build understanding of operational datasets for usability enhancements.
• Curate and maintain reusable datasets for self-service.
• Provide guidance and training for business users.
• Refine query performance and storage structures in the platform.
• Handle complex analytical builds for optimization.

Conocimientos

Azure

Databricks

SQL

Python

Power BI

Data modeling

Clear communication

Educación

Degree in a technical or quantitative field

Herramientas

dbt

Descripción del empleo

Do you love turning raw data into powerful, easy‑to‑use analytics that help people work smarter? Are you passionate about enabling others to self‑serve insights, mastering modern data tools, and bringing engineering discipline into the analytics space? Then our Analytics Engineering & Self‑Service team wants to hear from you! We are a curious, collaborative, and impact‑driven group focused on building a trusted analytics foundation and empowering teams across the organization to explore data with confidence.

In this role, you’ll help shape a modern self‑service ecosystem on Azure, Databricks, dbt, and Power BI. You will work closely with business analysts, data engineers, product teams, and power users—supporting them, coaching them, and helping them unlock the full value of our data. If you’re excited about scaling data literacy, improving platform usability, and applying engineering best practices to analytics, this is the place for you.

What You Will Do

• Dataset mastery: Build a deep understanding of our operational and content datasets and use this knowledge to shape analysis‑ready structures that improve usability across Azure, Databricks, Power BI, and complementary tools including dbt.
• Asset curation & ingestion ownership: Curate and maintain high‑quality, reusable datasets and analytical components while handling the ingestion and preparation of new data sources, ensuring they are structured, documented, and ready for self‑service use across the organization.
• User support and enablement: Provide hands‑on guidance to business users as they build their own dashboards, reports, and analyses; deliver targeted training and documentation that strengthen their platform skills and analytical autonomy.
• Platform optimization: Monitor and refine query performance, storage structures, and workspace configurations across Databricks, Azure, and Power BI to ensure a smooth, cost‑efficient, and scalable self‑service experience for all users.
• Strategic analytics delivery: Handle high‑complexity analytical builds when business users cannot complete them independently, using these cases to improve platform capabilities and inform future training and enablement priorities.

What You Bring

• Technical foundation: Strong experience working with Azure, Databricks, SQL, Python and Power BI, with the ability to build reliable analytical structures and apply solid development practices across the self‑service platform. Exposure to transformation tools such as dbt or similar frameworks is a plus.
• Data modeling & preparation: A solid background in preparing and shaping data for analytics using modern patterns such as the medallion architecture, including ingesting new data sources and ensuring they meet quality, structure, and usability expectations.
• User‑focused enablement: Clear communication skills and the ability to translate technical concepts into practical guidance for business users, including delivering training sessions and offering direct hands‑on support as they build their own dashboards and analyses.
• Quality & maintainability mindset: Experience applying testing, documentation, version control, and lightweight validation routines that improve data reliability, reduce rework, and keep analytical assets maintainable over time.
• Platform and performance awareness: Practical understanding of how to monitor, tune, and configure analytics environments—particularly in Databricks and Power BI—to ensure smooth performance, good user experience, and cost‑efficient operations.
• Education & domain experience: A degree in a technical or quantitative field—or equivalent professional experience—combined with fluent English and, ideally, some familiarity with financial or market‑related datasets.