This role requires a pragmatic engineering mindset. You will be responsible for architecting robust data pipelines, designing scalable transformation logic, and building the MLOps infrastructure required to deploy, monitor, and scale machine learning workflows in production.

• Design, build, and maintain scalable, fault-tolerant data pipelines (ETL/ELT) to ingest and process large-scale structured and unstructured data using Spark and cloud-native architectures
• Collaborate closely with Data Scientists to transition experimental models into clean, production-ready code and robust pipelines
• Implement advanced data modeling and transformation logic to ensure high-fidelity inputs for both downstream models and business analytics
• Build continuous integration and deployment pipelines for data and ML workflows, ensuring system reliability, data quality, and uptime

Tech Stack:

• Languages&Frameworks: Scala, Python, SQL, Spark (with experience using Spark in production environments)
• Cloud&Infra: Azure Cloud Platform, Databricks, Kafka
• CI/CD&IaC: Azure DevOps / GitHub, Terraform

What you will need

• 7+ years of experience in data engineering, ideally with hands-on exposure to analytics engineering practices (e.g., data modeling, transformation logic)
• Proven experience working closely with data scientists or driving data science projects with a highly pragmatic, production-focused mindset
• Deep understanding of data pipeline orchestration, distributed processing, and building resilient, testable ETL/ELT systems
• Solid grasp of data modeling concepts, especially in the context of analytics and reporting (conceptual, logical, and physical models)

What you will bring

• Ability to explain complex technical concepts to both technical and non-technical stakeholders
• Strong ability to work effectively with cross-functional teams including data science, engineering, and business units