Senior Data Engineer
Role details
Job location
Tech stack
Job description
In this role, as a Senior Data Engineer, you will play a key role in shaping and evolving the foundation of scalable data platforms. You will design and implement high-performance infrastructure to ingest, process, and serve large volumes of data, ensuring performance, reliability, and cost efficiency across cloud environments. Your focus will be on building high-quality, reusable data pipelines and frameworks that enable analytics, AI initiatives, and business insights at scale.
You will collaborate closely with cross-functional teams-Data Analysts, Machine Learning Engineers, and Software Developers-to establish data governance, standardize best practices, and drive a culture of data-driven decision-making. As a technical reference within the team, you will also mentor junior engineers and contribute to the long-term architectural roadmap of the company's data ecosystem.
Residing in Spain and being open to an independent contractor (freelance) engagement is mandatory What To Expect In This Role (Responsibilities)
- Design, architect, and evolve scalable infrastructure to ingest, transform, and serve data efficiently.
- Develop, maintain, and optimize data pipelines and frameworks for batch and real-time processing.
- Ensure data quality, consistency, and observability across systems and workflows.
- Lead improvements in existing pipelines to enhance performance, reliability, and cost-effectiveness.
- Implement and enforce strong data governance practices that promote accessibility and trust in data assets.
- Collaborate with engineering and analytics teams to define data models, schemas, and ETL/ELT strategies.
- Investigate and resolve complex data issues, maintaining robustness and system resilience.
- Stay up to date with industry trends and propose innovative solutions to enhance the data architecture.
Requirements
- 8+ years of proven experience as a Data Engineer or Backend Developer in data-intensive environments.
- Strong proficiency in Python and SQL for building scalable data pipelines.
- Solid experience with distributed data processing frameworks such as Apache Spark, Hadoop, Hive, or Presto.
- Deep understanding of data modeling, transformation, and data warehouse design principles.
- Hands-on experience with cloud platforms such as AWS, GCP, or Azure (S3, Redshift, Glue, Lambda, EMR, etc.).
- Familiarity with data governance, data quality monitoring, and metadata management practices.
- Proficiency in ETL/ELT frameworks and workflow orchestration tools.
- Experience with version control systems (GitHub or similar).
- Strong analytical thinking, problem-solving skills, and attention to detail.
- Advanced or fluent level of English (written and spoken).
Nice to Have
- Experience in data architecture and design for scalability.
- Familiarity with containerization and orchestration tools (Docker, Kubernetes).
- Exposure to Airflow, Prefect, or similar orchestration tools.
- Understanding of data lakehouse architectures.
