Data Engineer - Azure

Responsibilities:

• Design, build, and optimize scalable batch and streaming data pipelines using distributed frameworks like Spark/Databricks/Kafka.
• Data and ML Engineering thought leadership (What, Why, and How).
• Design and Code - robust data models, feature pipelines, and ETL/ELT frameworks for analytics and ML.
• Ensure data quality, observability, lineage, and performance across data platforms.
• Build and refine ML models end to end: feature engineering, training, evaluation, and deployment.
• Partner with data scientists to convert prototypes into production-grade ML solutions.
• Implement CI/CD, model versioning, monitoring, and automation across data and ML workflows.
• Product-Driven Mindset: Collaborate with engineering and product teams to deliver data-driven outcomes.

Requirements:

• 7+ years of experience in ML-Data Engineering development. Strong SQ/NoSQL, Python, PySpark, and ML Models Lifecycle and Frameworks (MlFlow, Spark-ml), and Orchestration (Airflow/Oozie/Dagster, etc. ).
• Expertise in Big Data modeling, Distributed processing, and Lake and Warehouse architectures at large operational scale.
• Hands-on with ML lifecycle tools (MLflow, Feature Store, model monitoring, Evaluation).
• Strong Analytical and Problem-Solving Skills - Data/Process-Intensive Design/Architecture.
• Strong debugging and optimization. Basic hold on foundational modeling concepts and algorithms such as - Regression, Classification and Statistical models.
• Good Hold on Concepts- Distributed File Formats, Open table Formats, Distributed transaction management, and Workload Parallelizing.
• Hands-on: Unix, Hadoop, Object store fundamental operations, and commands.
• Basic skills with containerized processing (Docker + K8s).
• Experience in data engineering (SQL, Python, Databricks).
• Strong understanding of data modeling and data structures.
• Basic to intermediate knowledge of semantic technologies (RDF, OWL, knowledge graphs).
• Experience with data pipelines and analytics/reporting (e. g., Power BI).
• Understanding of data integration and interoperability concepts.