Data Engineer / Data Scientist

This role offers the opportunity to build scalable data and machine learning solutions within a technology-driven, collaborative environment. You will design and optimize batch and streaming data pipelines using Python, PySpark, and Azure Databricks. The position combines data engineering with machine learning, MLOps, and model deployment to support impactful analytics and AI initiatives. You will work extensively with Azure services, Spark, Delta Lake, APIs, and cloud-based data architectures. As an individual contributor, you will take ownership of assigned deliverables while partnering closely with technical and business teams. The role is well suited to an experienced data professional who enjoys solving complex data challenges and working across modern cloud and ML technologies.

The ideal candidate brings 5–8 years of relevant experience across data engineering, data science, machine learning, or cloud analytics, with strong hands-on capabilities in Python, PySpark, Azure, and Databricks. You should be comfortable developing production-ready data solutions, troubleshooting distributed processing workloads, and contributing to machine learning and MLOps initiatives.

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Information Technology, or a related discipline.
• 5–8 years of relevant professional experience in data engineering, data science, machine learning, or cloud analytics.
• Strong hands-on expertise in Python and PySpark.
• Good knowledge of Microsoft Azure and Azure Databricks.
• Hands-on experience with MLOps practices and tools.
• Practical experience supporting machine learning projects.
• Basic understanding of machine learning model deployment.
• Strong experience developing and debugging Spark-based applications.
• Hands-on experience with Databricks notebook development.
• Strong knowledge of Spark DataFrames using PySpark or Scala.
• Experience optimizing Spark jobs and Databricks workloads.
• Experience developing APIs using Python or Scala.
• Working knowledge of Azure Event Hubs, Storage Accounts, Key Vault, Service Bus, Azure Functions, and Azure Data Lake Storage.
• Understanding of Databricks job clusters and compute configurations.
• Experience implementing cloud-based data solutions on Azure.
• Knowledge of real-time streaming technologies such as Kafka.
• Experience developing batch and streaming pipelines using Event Hubs, Kafka, or IoT data sources.
• Hands-on experience implementing Delta Lake solutions.
• Working knowledge of GitHub or similar version-control platforms.
• Exposure to MLflow or comparable tools for experiment tracking and model lifecycle management is beneficial.
• Experience with CI/CD for data and machine learning workloads is a plus.
• Knowledge of data quality validation, monitoring, and production support is advantageous.
• Strong analytical and problem-solving abilities.
• Ability to work independently while collaborating effectively with cross-functional project teams.