About Us:
Building robust, cloud-native data infrastructure that supports machine learning and analytics workflows at scale is a complex task that requires hands-on experience with Databricks.
We're seeking experienced senior software engineers who possess a deep passion for technology and an eagerness to tackle technical challenges. You have a strong interest in leveraging AI and cutting-edge methodologies to drive efficiency and precision in your work.
A strong commitment to the organization's growth and the advancement of its internal ventures is critical. You care about your project, but also about development communities and their growth. Sharing best practices with engineering communities across projects is paramount to you.
You must demonstrate a commitment to continuous learning, allocating time to acquire new skills and obtain certifications to validate your expertise and experience.
This role is ideal for someone who has hands-on experience with Databricks, enjoys building efficient and scalable data pipelines, and is eager to grow their platform skills in a dynamic, multi-cloud environment.
As a Data Engineer, you will be part of a cross-functional team including platform engineers, DevOps, and data scientists, collaborating on ingestion, transformation, orchestration, and data reliability for production-grade pipelines.
Your responsibilities will include building and maintaining scalable ETL/ELT pipelines on Databricks using Spark, Delta Lake, and Python. You will also develop orchestration logic using tools such as AWS Step Functions, Lambda, or Databricks Workflows.
Additionally, you will contribute to medallion architecture layers (Bronze, Silver, Gold) for structured data processing, collaborate on infrastructure provisioning and pipeline automation using Terraform and GitHub Actions, and troubleshoot Spark job performance to ensure reliable, efficient data pipelines.
You will support cross-functional teams (data scientists, ML engineers) by delivering curated, production-ready data. Your required skills and qualifications include 3–6 years of experience in data engineering or data platform roles.
You should have solid experience with Databricks and Delta Lake, including job and cluster setup. You should also be strong in PySpark, SQL, and scripting for data transformation, and familiar with AWS services: S3, Lambda, Step Functions, IAM, CloudWatch.
Familiarity with CI/CD practices and infrastructure automation using Terraform is also essential. Nice-to-have skills include familiarity with Unity Catalog, MLflow, or other MLOps tools, and experience designing pipelines in multi-tenant or highly-regulated environments.
The benefits of this role include remote work options, coworking space financial coverage, flexible working hours, B2B with multiple benefits, paid days off annually, workspace program, English language lessons on all levels, performance financial incentives, paid courses and certifications, and participation at international conferences.
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