HybridSeniorData Engineerposted 6d ago

✦Role summaryAI-generated

The role involves designing and maintaining scalable data platforms and pipelines using Spark, Databricks, and cloud services on Azure and AWS. Collaboration with analysts and engineers is required to ensure data quality, performance, and CI/CD integration.

Skills required

Spark Databricks Azure AWS Python SQL Airflow Data quality Data pipelines CI/CD Data engineering Delta Lake Terraform Git MLflow Docker scikit-learn SciPy Feature engineering Data governance AI

About this role

• 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