Data EngineerRemote
GM01 - Data Engineer (DataBricks)
REMOTEPosted January 9, 2026
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About the Role
We are looking for a Data Engineer to help design, build, and scale a modern data engineering platform based on Databricks and cloud-native technologies. This is a hands-on role focused on building robust, production-grade data pipelines that support analytics, product features, and advanced decision-making use cases. You’ll work closely with engineering, analytics, and product stakeholders to transform raw data into trusted, high-quality datasets. The ideal candidate enjoys solving complex data problems, optimizing large-scale processing jobs, and contributing to architectural decisions while still writing high-quality, maintainable code.
Role and Responsibilities
- Design and implement scalable data pipelines using Databricks (Apache Spark, Delta Lake).
- Build and maintain batch and near–real-time ETL/ELT workflows from multiple data sources.
- Develop optimized data models to support analytics, reporting, and downstream applications.
- Ensure data reliability through validation, monitoring, and data quality checks.
- Optimize Databricks workloads for performance and cost efficiency.
- Collaborate with analytics and product teams to enable advanced analytical and data-driven workflows.
- Build and maintain orchestration workflows using modern scheduling tools.
- Leverage cloud services (AWS or equivalent) for storage, compute, and access control.
- Contribute to best practices around data architecture, documentation, testing, and governance.
- Support internal data consumers with well-structured, discoverable datasets.
What We’re Looking For
Must-Haves
- B2/C1 English level
- 4+ years of experience as a Data Engineer or similar role
- Strong hands-on experience with Databricks (Spark, Delta Lake, job orchestration)
- Excellent SQL skills for data transformation and analytics
- Proficiency in Python for data engineering workflows
- Experience building and operating production data pipelines
- Familiarity with modern data architectures (lakehouse, ELT patterns)
- Experience working with cloud platforms (AWS preferred)
- Understanding of data quality, schema evolution, and pipeline reliability
- Ability to collaborate closely with cross-functional teams
Nice-to-Haves
- Experience with streaming or near–real-time data processing
- Familiarity with data orchestration tools (Airflow, Databricks Workflows, etc.)
- Exposure to data governance, catalogs, or lineage tools
- Experience supporting analytics, BI, or ML workloads
- Background in high-scale, data-intensive systems
Why This Role Is Exciting
- Hands-on ownership of a modern data platform
- Strong focus on Databricks and lakehouse architecture
- Complex, real-world data challenges at scale
- High autonomy and influence over data engineering decisions
- Opportunity to improve data reliability, performance, and usability
- Close collaboration with product, analytics, and engineering teams
Application Instructions
Submit everything [here]:
- Your résumé/CV highlighting Databricks, Spark, SQL, and cloud experience
- Links to technical projects or repositories (if available)
- Your availability and compensation expectations
- Any experience building or scaling modern data platforms