Job Title

We are seeking a highly skilled and experiencedData Engineerbased in India to join our growing Data Science team. The ideal candidate will play a critical role in designing, building, and optimizing our data pipelines and infrastructure to support advanced analytics and machine learning initiatives. This individual will work closely with data scientists, analysts, and other engineers to ensure reliable, scalable, and high-quality data solutions.Key Responsibilities: Design, develop, and maintain robust data pipelines to ingest, process, and transform large datasets from diverse sources. Optimize SQL queries and database performance for efficient data storage and retrieval. Collaborate with data scientists to support data preparation and model deployment workflows. Develop and maintain ETL processes to ensure data integrity, consistency, and availability. Monitor data workflows and troubleshoot issues in ata pipelines and infrastructure. Contribute to data architecture planning and documentation. Ensure compliance with data governance and security standards.Requirements: Minimum 5 years of professional experiencein data engineering or a related role. Proficiency in Microsoft SQL Server (MS SQL) , including writing complex queries, stored procedures, and performance tuning. Strong programming skills in Python , particularly with libraries relevant to data engineering(e.g.,Pandas, SQLAlchemy, Airflow). Bachelor's degreein computer engineering, Computer Science, or a related field—or equivalent practical experience. Experience working with structured and unstructured data, APIs, and cloud data storage. Familiarity with data warehouse concepts and data modeling techniques. Proven ability to work independently and as part of a distributed team. Experience with cloud platforms (Azure, AWS, or GCP).Preferred Qualifications: Experience in the healthcare industryor with healthcare-related data systems. Familiarity with data orchestration tools like Apache Airflow or similar. Exposure to version control tools (e.g., Git) and DevOps practices. Knowledge of basic data science workflows and model operationalization.