Data Engineer/Data Warehouse Expert - ABDM, NHA

Data Engineer/Data Warehouse Expert - ABDM, NHA

About the role:

NHA is seeking a talented and motivated Data professional with hands-on experience in data related technologies to drive and implement data driven insights. The candidate should have a proven track record of managing large databases with deep knowledge of databases operations, performance tuning, data migration, building data flow pipelines, data warehouse and data lake setup.

Qualifications

• B.E/B.TECH OR MCA from a reputed college/University.

Technical Experience and Qualifications:

• Overall, minimum 10 years of total IT experience post qualification completion.
• Strong SQL skills and experience working with relational and NoSQL databases like PostgreSQL, Mongo dB etc.
• Design, build, and maintain scalable and efficient data pipelines and ETL processes to support data analytics and reporting needs.
• Hands on Data Modelling, Data warehouse design and implementation experience.
• Working with Cloud vendors hands on experience on tools like AWS Glue, Redshift, EMR with deep understanding of cloud infra and capabilities.
• Data migration experience from on-Prem to Cloud.
• Knowledge of data security practices and database backup and archival processes.
• Excellent understanding on DB concepts, performance tuning etc.
• Experience in building Datawarehouse, data lake solutions with Hadoop.
• Expertise on Python-Pandas, Spark tools, ability to handle Parquet, ORC, AVRO file formats.
• Experience on data quality tools, data governance best practices.
• Excellent communication, ability to articulate and leadership skills.

Certifications:

• AWS Certified data engineer or Google's Cloud Data Engineer Professional Certificate or Microsoft Azure Data Engineer is preferred.

Key Mandate:

• Design, build, and maintain scalable and efficient data pipelines and ETL processes to support data analytics and reporting needs.
• Collaborate with cross-functional teams to understand data requirements and translate them into technical solutions.
• Implement data quality and governance processes to ensure accuracy, completeness, and reliability of data.
• Review and implement database best practices, help build processes to make data driven organization.
• Review and implement the disaster recovery procedures.
• Identify and address performance bottlenecks and optimization opportunities in data processing and storage systems.
• Stay up to date with emerging technologies and best practices in data engineering and recommend innovative solutions to enhance our data infrastructure.