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Data Engineer

hace 2 meses

Gasteiz Vitoria, Álava, España Nielseniq A tiempo completo
About NielsenIQ

NielsenIQ is the world's leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth.

In 2023, NielsenIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach.

Founded in Barcelona in 2001, NielsenIQ is the data collection provider of choice for the Market Research Industry.

A tech-driven company built on a robust and engaged panel and a strict quality standard (ISO).

We make possible a truly 360 degrees view of today's consumers collecting behavioral and declarative data from a single source, our panelists, in 23 countries across the globe.

Leveraging our data, savvy researchers better define winning strategies based on an in-deep understanding of consumers' preferences and behaviors.

About the Role

As a Data Engineer, you will be part of the Data team, covering the three main specialties in the data field: Data Science, Data Analytics, and Data Engineering.

We are looking to strengthen our Data Engineering capabilities by adding a skilled Data Engineer to our team.

We seek a person who brings practical experience and enthusiasm to help drive our data initiatives forward.

This role involves understanding different stakeholders' needs, sharing decisions with the team, and explaining data solutions clearly.

Key Accountabilities

The accountabilities of a Data Engineer include adhering to applicable standards and guidelines while solving complex problems.

The Data Engineer is expected to work within their team scope and contribute to the harmonization and improvement of solutions across the organization.

The Data Engineer is also accountable for sharing knowledge with other engineers during pair programming/pair work, when sought for advice, during code reviews, and by participating in Communities of Practice.

The Data Engineer is responsible for bringing together technical assets from different disciplines within their cross-functional team and fostering a common solution approach among team members with diverse technical backgrounds.

Qualifications

Experience in designing and implementing data pipelines.

Ability to address problems with scale-up and scale-out approaches and combine them in data pipelines.

Understanding of Data Architecture principles.

Awareness of Data Governance practices.

Consideration of economic aspects related to the operability of data pipelines.

Maintenance of data integrity during reprocessing.

Understanding how customer data deliveries (internal or external) are affected by software changes.

Familiarity with data design patterns and best practices.

Knowledge of data modeling, data schemas, and their evolution.

Awareness of data transformation design patterns.

Focus on data integrity in crafting data pipelines, including monitoring data health and state of intermediate data and alerting mechanisms.

Designing Quality Control/Validation processes / Quality Gates.

Technologies and Standards

Proficiency in Python and SQL.

Familiarity with scheduling tools like Apache Airflow.

Interest in Big Data solutions, such as Apache Spark.

Understanding of Data Engineering techniques.

Ability to maintain and extend processes and functionalities.

Knowledge of application packaging and delivery mechanisms, including CD/CI best practices and security concerns.

Ability to integrate partial solutions and RnD results into production-ready data pipelines.

Experience in building and operating infrastructure (e.g., for testing purposes).

Ability to support operational questions for production environments.

Understanding of cloud infrastructure and services for setting up and maintaining data pipelines (e.g., AWS).

Advising on data transformation workflow designs and optimizations focusing on cost, performance, and delivery mechanisms (streams/increments/etc.).