Position Summary

Data Engineer with strong expertise in managing & building scalable data pipelines and solutions using PySpark, SQL, and AWS. The ideal candidate will have hands-on experience in designing, developing, and optimizing data workflows to enable high-quality, reliable, and timely data delivery for business use cases.

Job Responsibilities

Design, develop, and maintain data pipelines and ETL workflows using PySpark and SQL.

Work with large-scale datasets to ensure data accuracy, consistency, and quality.

Implement and manage data solutions on AWS Cloud services (e.g., S3, Glue, EMR, Redshift, Lambda).

Collaborate with Business Analysts and business stakeholders to understand requirements and deliver robust solutions.

Optimize queries and data processes for performance and scalability.

Guide team to Troubleshoot and resolve issues in existing data workflows and pipelines

Education BE/B.TechMaster of Computer ApplicationWork Experience

Proven 6+ years of experience in following:

Building production-grade data pipelines using PySpark.

Strong proficiency in SQL (query optimization, data modeling, performance tuning).

Hands-on experience with AWS cloud services for data engineering.

Solid understanding of data warehousing concepts and experience with tools like Redshift, Snowflake, or similar (nice to have).

Experience with version control (Git), CI/CD, and Agile methodologies.

Strong problem-solving skills and ability to work independently as well as in a team

Exposure to pharma / healthcare domain

Preferred Skills (Good to Have):

Experience with workflow orchestration tools (e.g., Airflow, Step Functions).

Knowledge of data governance, data quality frameworks, and metadata management.

Behavioural Competencies OwnershipTeamwork & LeadershipCultural FitMotivation to Learn and GrowTechnical Competencies Problem SolvingLifescience KnowledgeCommunicationCapability Building / Thought LeadershipSkills