About the role
Job Description
• Design, develop, and maintain scalable and reliable data platforms and pipelines for analytics and operational use cases • Build distributed data processing workflows using Apache Spark, pySpark, and Databricks • Develop and evolve cloud-native data solutions leveraging Azure and AWS services • Model and optimize data structures for analytics, reporting, and downstream consumption • Integrate data from multiple heterogeneous sources while ensuring data quality, consistency, and traceability • Implement data quality controls, schema validation, lineage tracking, and monitoring processes • Optimize performance, scalability, reliability, and cost-efficiency of data pipelines and infrastructure • Collaborate closely with scientists, analysts, and engineering teams to translate business and research requirements into technical solutions • Contribute to CI/CD processes, infrastructure automation, and engineering best practices • Research and introduce new technologies and approaches within the data engineering ecosystem
Qualifications
• 3+ years of commercial experience in Data Engineering • 2+ years of hands-on production experience with Databricks • Strong expertise with Delta Lake, including medallion architecture and table optimization • Advanced proficiency in Python, pySpark, and SQL • Deep practical knowledge of Databricks components including Unity Catalog, Lakeflow Jobs / Workflows, and job optimization • Hands-on experience with AWS services including S3, IAM, KMS, and at least one of AWS Batch, ECR, EventBridge, Lambda, or API Gateway • Experience designing event-driven and asynchronous data pipelines • Strong knowledge of Infrastructure as Code and CI/CD practices • Practical experience with Terraform and Databricks Asset Bundles • Experience working with Git workflows, pull request reviews, and automated testing • Understanding of secure authentication approaches such as OIDC and service principals • Practical experience with MLflow, experiment tracking, model registry, and reproducibility practices • Strong understanding of data quality, lineage, schema contracts, and provenance concepts • Experience with data quality frameworks such as Great Expectations, Soda, pyDeequ, or Databricks DQX • Experience monitoring and optimizing data platform reliability and cloud costs • Upper-Intermediate or higher English level
WILL BE A PLUS:
• Experience packaging workloads using Docker and running batch workloads in AWS • Familiarity with ECR and containerized data processing solutions • Experience with experiment optimization libraries such as scikit-optimize, BoTorch, Ax, or scipy • Understanding of ML feature engineering and leakage prevention techniques • Experience working in regulated or high-security environments • Knowledge of data governance, compliance, and data residency requirements • Experience disabling or restricting AI-assisted tooling/features in secure environments
Additional Information