Data Engineer / DataOps Engineer
Remote work: fully remote.
Assignment type: B2B.
Project language: English.
Project length: > 12 months + prolongations.
Start: ASAP / 1 month.
Workload: full time.
Recruitment process: 2 interviews with the client.
Industry: IT Services / Digital Consulting
Additional information: After receiving the offer, a background check is carried out (references, criminal record check, etc.).
Summary: The primary purpose of the Data Engineer / DataOps Engineer role is to develop and manage data pipelines and workflows, enhancing the organization's data platform capabilities. This position is crucial for ensuring that data is processed reliably and efficiently, enabling better decision-making across the company.
Responsibilities:
Develop and maintain end-to-end data pipelines using Snowflake as the core data platform.
Build ELT workflows using dbt and manage orchestration with Airflow.
Implement and support DataOps processes, including CI/CD automation, monitoring, and workload deployment on Kubernetes.
Optimize Snowflake performance, including warehouses, storage usage, and query efficiency.
Ensure data reliability through data validation, testing, and monitoring practices.
Integrate various data sources and manage ingestion processes into Snowflake.
Collaborate with cross-functional teams to deliver reliable, production-ready data solutions.
Follow engineering best practices, maintain coding standards, and support continuous improvement.
Support team knowledge sharing and mentor junior developers when needed.
Key Requirements:
5+ years of professional practice in data engineering.
Strong, practical experience with Snowflake (views, tables, performance tuning, orchestrated ELT processes).
Solid expertise using dbt for SQL-based transformations.
Hands-on experience with Airflow for workflow scheduling and automation.
Experience deploying and maintaining containerized workloads on Kubernetes.
Familiarity with cloud environments, with strong understanding of Microsoft Azure services.
Practical experience building ETL/ELT pipelines and maintaining production data workflows.
Good understanding of Git-based development, CI/CD pipelines, and general DevOps principles.
Analytical mindset and ability to troubleshoot issues in complex systems.
Nice to Have:
Experience with event streaming or messaging systems.
Familiarity with data quality tools.
Exposure to observability or platform engineering tooling.
Understanding of MLOps concepts or ML workflow integration.