Data Engineering / Platform Engineer
About the Role:
The primary goal of the Data Engineering team is to design, build, and maintain a high-performance, self-service data platform that empowers Analytics Engineers, Data Analysts, and Data Scientists. This platform enables teams to create reliable, scalable, and performant data solutions while providing abstractions for quality, security, lineage, profiling, documentation, and observability. You will work in a challenging big data ecosystem, focusing on storage efficiency, scalable queries, extensibility, and flexibility, with the goal of enabling robust data-driven decision-making across the company.
Responsibilities:
* Design, implement, and maintain a scalable, high-performance data platform that supports analytics, machine learning, and internal applications.
* Collaborate with stakeholders across the company to understand needs and translate them into extensible, incremental, and maintainable solutions.
* Build and optimize frameworks, libraries, and utilities for data ingestion, processing, transformation, and observability.
* Ensure platform reliability through monitoring, logging, testing, and proactive issue resolution.
* Define strategic platform vision, crossing team boundaries to solve complex problems and enable efficient data operations.
* Produce comprehensive documentation of platform features, processes, and best practices.
* Conduct code reviews, promote software engineering excellence, and ensure operational quality in all data-related projects.
* Provide Level 2 and Level 3 support for internal libraries, tools, and services.
What we are looking for:
* Solid understanding of Big Data technologies and frameworks (Spark, Trino, Hive, Iceberg, Delta Lake, Hudi) and proficiency in programming languages such as Python and YAML.
* Hands-on experience with Databricks or similar platforms.
* Knowledge of Data Processing Architectures (Lambda, Kappa, Event Sourcing).
* Strong software engineering skills:
clean, maintainable, and scalable code.
* Experience with data orchestration and workflow management tools (Airflow, Dagster, Prefect).
* Familiarity with data lifecycle, governance, lineage, privacy, retention, and anonymization.
* Knowledge of cloud infrastructure, containerization, and infrastructure as code is a plus (AWS, GCP, Azure, Docker, Kubernetes).
* Strong focus on performance, observability, and reliability of data systems.
* Excellent communication skills;
able to share knowledgeproactively and collaborate across teams.
Languages
* Fluent English, both written and spoken.
Location:
* Remote