Associalte Specialist , Data Engineering

Merck; India; Hybrid; INR 1,000,000 - 1,400,000; Full time

Job summary

Merck, Hyderabad Tech Center, is seeking an Associate Specialist: Data Engineering. The role blends hands-on data pipeline work with collaboration across data analysts and scientists.

The candidate will build and maintain ETL/ELT workloads, leverage PySpark, SQL, and Databricks, and contribute to lakehouse architectures in a hybrid work setup (3 days onsite, 2 remote). The ideal candidate has 2-4 years of practical data engineering experience, strong Python and SQL skills, and familiarity with

Qualifications

• Bachelor's degree or equivalent practical experience.
• 2-4 years in data engineering building production pipelines.
• Hands-on with AWS services and security basics.
• Experience with Databricks, Spark, PySpark, lakehouse concepts.
• Strong SQL and Python skills for data processing.

Responsibilities

• Build and support batch and streaming data pipelines from designs.
• Develop ETL/ELT transformations using Python, PySpark, SQL.
• Collaborate with analysts, scientists, engineers, and product managers.
• Implement data quality checks and monitoring across pipelines.
• Run, monitor, and troubleshoot pipelines with orchestration tools.
• Contribute to cloud provisioning with Terraform and CI/CD pipelines.
• Document pipelines, data contracts, and runbooks.

Skills

Python

PySpark

SQL

AWS

Databricks

Delta Lake

Education

Bachelor's degree in computer science/Engineering

Tools

Databricks

Apache Spark

GitHub

CI/CD

Terraform

Docker

Job description

Job Description

Associate Specialist: Data Engineering

The Opportunity:

Join a global biopharma company with a 130-year legacy and mission to achieve new milestones in healthcare. Be part of a technology-driven, data-led organization supporting a diversified portfolio of medicines, vaccines, and animal health products. Work alongside passionate teams that use data, analytics, and insights to drive decisions and tackle some of the world's greatest health threats.

Our Technology Centers are globally distributed hubs that enable our digital transformation and business outcomes across IT. They bring together diverse teams to collaborate, share best practices, and deliver solutions that save and improve lives.

This role is based at our Hyderabad Tech Center and follows a hybrid working model (3 days onsite, 2 days remote). Candidates are expected to reside within commuting distance of the Hyderabad office.

Role Overview

We are looking for a Data Engineer with 2-4 years of hands-on experience in building and supporting data pipelines, ETL/ELT workflows, and analytics-ready datasets. The ideal candidate should have strong fundamentals in Python , PySpark , SQL , AWS , and Databricks , with practical exposure to data lakes, lakehouse patterns, data warehousing, data quality, and production support. This role is best suited for a hands-on engineer who can work from defined requirements, contribute to reliable data solutions, collaborate with cross-functional teams, and grow into larger ownership over time.

What will you do in this role

• Build, enhance, and support batch and streaming data pipelines using defined technical designs and backlog requirements.


• Develop and maintain ETL/ELT transformations using Python , PySpark , and SQL across data lake, lakehouse, and warehouse environments.


• Work closely with Data Analysts, Data Scientists, senior engineers, tech leads, and product managers to understand requirements and deliver curated, analytics-ready datasets.


• Implement data quality checks , validations, reconciliations, and basic anomaly checks to improve trust and usability of data outputs.


• Run, monitor, and troubleshoot pipelines using orchestration and observability tools such as Databricks Workflows , AWS Step Functions , scheduling, logging, monitoring, and alerting.


• Follow engineering practices including unit testing , integration testing , automated data tests , code reviews, and quality gates within CI/CD.


• Support BI and analytics use cases by applying dimensional modeling concepts such as facts, dimensions, star/snowflake schemas, and slowly changing dimensions (SCD).


• Write and tune SQL queries for data profiling, transformations, validations, debugging, and performance improvements.


• Use AWS services such as S3 , Glue , Lambda , Step Functions , EMR , and CloudWatch to support data engineering workloads while following security practices such as IAM, encryption, and least privilege.


• Contribute to cloud resource provisioning and environment configuration using Terraform , with guidance from senior engineers.


• Package, deploy, and support workloads using Docker and related runtime configurations, including ECS/Fargate where applicable.


• Use GitHub for version control, branching, pull requests, code reviews, and contribution to CI/CD pipelines.


• Develop scalable data processing logic on Databricks / Apache Spark using PySpark and lakehouse concepts such as Delta Lake , ACID transactions, and schema evolution.


• Use Jupyter/Databricks notebooks for exploration, debugging, and PoCs; convert validated logic into reusable modules, tests, and deployment-ready pipelines.


• Participate in Agile delivery ceremonies, provide task-level estimates, share progress updates, and raise risks or dependencies early.


• Create and maintain technical documentation such as pipeline specifications, data contracts, runbooks, and support notes.

What Should you have

• Bachelor's degree in computer science, Engineering, or a related field, or equivalent practical experience.


• 2-4 years of hands-on experience in data engineering, including building or supporting production data pipelines and ETL/ELT workflows.


• Practical experience with AWS services such as S3 , Glue , Lambda , Step Functions , EMR , and CloudWatch; understanding of IAM, encryption, and cloud security basics.


• Hands-on experience with Databricks , Apache Spark , PySpark , and lakehouse concepts such as Delta Lake.


• Strong SQL skills for joins, window functions, data profiling, transformations, validations, and performance tuning.


• Good working knowledge of Python and PySpark , including Spark fundamentals such as partitioning, shuffle, caching, file formats, debugging, and optimization.


• Understanding of dimensional modeling concepts including facts, dimensions, star/snowflake schemas, and slowly changing dimensions (SCD).


• Exposure to GitHub , CI/CD , code reviews, branching, release practices, and engineering quality standards.


• Working knowledge of Do