Senior Data Engineer

Role Overview

We are looking for an experienced Data Engineer + BI professional with strong hands-on expertise in data engineering, cloud data platforms, Python, SQL, ETL, Big Data, and Business Intelligence. The ideal candidate will have 10+ years of experience in Data Engineering and Development, with strong experience building scalable data pipelines, working with AWS data services, developing data solutions using Python and PySpark, and delivering business insights through modern BI and visualization platforms.

Key Responsibilities

Design, develop, and maintain scalable data engineering and data processing solutions.

Build and optimize robust ETL/ELT pipelines using AWS Glue and other relevant data engineering tools.

Develop data processing solutions using Python and PySpark.

Work with AWS cloud services to build and maintain reliable, scalable data solutions.

Develop and maintain data pipelines integrating multiple data sources and systems.

Design and optimize SQL/RDBMS solutions, including PostgreSQL.

Work with Snowflake and DBT for data transformation, modeling, and analytics.

Develop and maintain data models and analytics-ready datasets.

Build dashboards, reports, and data visualizations using Tableau, Qlik, or similar BI platforms.

Collaborate with business and technical stakeholders to understand data requirements and deliver effective analytics solutions.

Follow data engineering, data quality, performance, and development best practices.

Must-Have Technical Skills

Data Engineering & Development

10+ years of hands-on experience in Data Engineering and Development.

Strong experience designing and developing scalable data pipelines and data processing solutions.

Strong understanding of data engineering concepts, ETL/ELT processes, data transformation, and data modeling.

SQL / RDBMS

7+ years of experience with SQL/RDBMS.

Strong hands-on experience with PostgreSQL.

Ability to write and optimize complex SQL queries.

Python

7+ years of demonstrable hands-on experience with Python.

Strong Python programming and data processing capabilities.

ETL & Big Data

Hands-on experience with ETL tools such as AWS Glue.

Strong experience with Big Data frameworks such as PySpark.

Experience developing and maintaining scalable data processing pipelines.

AWS Cloud

At least 3 years of hands-on AWS Cloud experience.

Experience with AWS Lambda, ECS, Step Functions, API Gateway, RDS, SQS, and DynamoDB.

Business Intelligence & Data Visualization

Experience with Tableau, Qlik, or similar Data Visualization/BI tools.

ThoughtSpot experience is preferred.

Ability to develop meaningful dashboards, reports, and visualizations from complex datasets.

Snowflake & DBT

Strong knowledge of Snowflake.

Hands-on knowledge of DBT for data transformation and data modeling.

Nice-to-Have Skills

AWS Certified Data Engineer certification.

Knowledge of Git and CI/CD practices.

Experience with GitLab or similar CI/CD tools.

Knowledge of Infrastructure as Code (IaC).

Experience with Terraform is preferred.

Pharma domain experience is a plus.

Experience with Agentic AI coding tools, such as VS Code Copilot or similar tools.

Preferred Candidate Profile

The ideal candidate should combine strong Data Engineering + AWS + Python + SQL + PySpark + BI expertise and be capable of working across the complete data lifecycle—from data ingestion and transformation to data modeling, visualization, and business insights. Strong communication, problem-solving, collaboration, and ownership skills are expected.