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AI Summary
The Senior Data Engineer will design, develop, and maintain scalable data pipelines using Python and PySpark for batch and streaming workloads, while building and optimizing data processing frameworks on cloud platforms like Databricks or Snowflake. Responsibilities also include designing robust data models, developing high-quality SQL code, and managing workflow orchestration using tools like Apache Airflow.
Role: Senior Data Engineer
Remote Work: INDIA
*Only Consultants local to INDIA are eligible.
*Only Consultants local to INDIA are eligible.
*No visa Sponsorship
Primary Responsibilities:
- Design, develop, and maintain scalable data pipelines using Python, PySpark, and other modern programming languages to support both batch and streaming workloads
- Build and optimize data processing frameworks on cloud platforms such as Databricks or Snowflake, ensuring performance, reliability, and cost efficiency
- Design and implement robust data models, including transactional (OLTP) and dimensional (OLAP) schemas, to support analytics, reporting, and application integration
- Develop high quality SQL code including complex queries, stored procedures, and views, with a focus on performance tuning and efficient data access patterns
- Create and manage workflow orchestration using Apache Airflow or similar tools, ensuring reliable scheduling, dependency management, and monitoring
- Implement and enforce data governance and metadata standards through tools such as Microsoft Purview, including data lineage, classification, cataloging, and security policies
- Build automated data quality and validation frameworks to ensure accuracy, completeness, and reliability of production datasets
- Collaborate with cross functional teams including data architects, analysts, scientists, and business stakeholders to understand requirements and deliver scalable, well designed data solutions
- Lead technical design sessions and code reviews, promoting engineering best practices, reusability, and maintainability
- Support cloud infrastructure and DevOps practices, including CI/CD pipelines, version control, testing automation, and environment management
- Monitor and troubleshoot production data pipelines, proactively addressing issues, performance bottlenecks, and system failures
- Contribute to the evolution of the enterprise data platform, recommending tools, frameworks, and architectures to improve scalability and efficiency
Required Qualifications:
- 5+ years of experience in data engineering, software engineering, or similar disciplines
- Hands-on experience with Databricks or Snowflake
- Experience with orchestration tools such as Apache Airflow
- Experience working with cloud ecosystems (Azure preferred; AWS/GCP acceptable)
- Advanced SQL skills and experience with OLTP and OLAP data modeling
- Solid understanding of modern data warehousing, data lake, and ELT/ETL design patterns
- Familiarity with data governance tools, especially Microsoft Purview
- Solid programming expertise in Python, PySpark, or similar languages
Preferred Qualifications:
- Healthcare industry experience, including claims, clinical, FHIR, HL7, or provider data
- Experience with containerization (Docker, Kubernetes) for data workloads
- Experience supporting machine learning workflows or analytical data science pipelines
- Knowledge of distributed computing concepts and performance tuning
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