Spcialist , Data Engineering

EyeBio; Hyderabad; Hybrid; INR 1,500,000 - 2,100,000; Full time

Job summary

EyeBio in Hyderabad is seeking a hands-on Data Engineer to design, build, and operate production-grade data platforms and pipelines. You will deliver governed, analytics-ready data using modern data warehousing and lakehouse patterns on AWS and Databricks, with emphasis on data quality, modeling, and scalable ETL/ELT.

You will collaborate with analytics, data science, and business stakeholders to translate requirements into robust datasets, applying testing, CI/CD, and observability best

Qualifications

• <=140 chars: Design, build, and operate batch and streaming data pipelines into AWS lakehouse/warehouse.
• <=140 chars: Develop and maintain ETL/ELT transformations using Python and PySpark.
• <=140 chars: Deliver analytics-ready datasets by partnering with analysts and data scientists.
• <=140 chars: Implement data quality controls, SLAs/SLOs, and metadata/lineage practices.
• <=140 chars: Use Databricks Workflows and AWS Step Functions for orchestration and observability.
• <=140 chars: Apply CI/CD, unit/integration tests, and data tests in production pipelines.
• <=140 chars: Model data with dimensional modeling and SCD; write advanced SQL for profiling.

Responsibilities

• <=140 chars: Design, build, and operate batch and streaming data pipelines to ingest data into AWS data lake/lakehouse and data warehouse.
• <=140 chars: Develop and maintain ETL/ELT transformations using Python and PySpark; optimize for performance.
• <=140 chars: Partner with Data Analysts/Scientists to deliver curated, analytics-ready datasets.
• <=140 chars: Implement data quality controls, SLAs/SLOs, and data catalog practices.
• <=140 chars: Use orchestration and observability tools (Databricks Workflows, AWS Step Functions).
• <=140 chars: Follow CI/CD, testing, and data quality gates; support governance and documentation.

Skills

Python

SQL

PySpark

Data pipelines

Data modeling

CI/CD

Agile

Tools

AWS

Databricks

Delta Lake

Terraform

Docker

GitHub

Job description

Job Description

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 hiring a hands-on Data Engineer who can design, build, and operate production-grade data platforms and pipelines end to end. You will deliver reliable, governed, secure, and analytics-ready data by implementing modern data warehousing and lakehouse patterns on AWS and Databricks, with strong focus on data quality, dimensional modeling, and scalable ETL/ELT. This role partners closely with analytics, data science, and business stakeholders to translate requirements into robust datasets, while applying engineering best practices such as testing, code reviews, CI/CD, and observability.

What will you do in this role

• Design, build, and operate batch and streaming data pipelines to ingest data from multiple sources into an AWS data lake / lakehouse and data warehouse.

• Develop and maintain ETL/ELT transformations using Python, PySpark, and SQL; optimize jobs for performance, cost, and reliability.

• Partner with Data Analysts, Data Scientists, and business stakeholders to understand use cases and deliver curated, analytics-ready datasets and features.

• Implement data quality controls (validation rules, reconciliation, anomaly checks), define SLAs/SLOs, and contribute to metadata, lineage, and data catalog practices.

• Use orchestration and observability to run pipelines reliably (e.g., Databricks Workflows, AWS Step Functions, scheduling, logging, monitoring, alerting).

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

• Model and publish data for BI/analytics using dimensional modeling (star/snowflake), facts & dimensions, and slowly changing dimensions (SCD).

• Write and tune advanced SQL for profiling, transformations, and performance troubleshooting across large datasets.

• Build on AWS using services such as S3, Glue, Lambda, Step Functions, EMR, and CloudWatch; follow security best practices (IAM, encryption, least privilege).

• Provision and manage cloud resources using Infrastructure as Code (e.g., Terraform) across dev/test/prod environments.

• Package and deploy workloads using Docker (and where applicable ECS/Fargate); manage dependencies and runtime configurations.

• Use GitHub for version control (branching strategies, pull requests, code reviews) and set up CI/CD for automated build, test, and deployment.

• Develop scalable processing on Databricks / Apache Spark using PySpark and lakehouse concepts (e.g., Delta Lake, ACID, schema evolution).

• Use notebooks (e.g., Jupyter/Databricks) for exploration and PoCs, then productionize solutions with reusable modules, tests, and deployment pipelines.

• Work in an Agile delivery model (planning, daily sync, reviews, retros), providing accurate estimates and proactively managing risks/dependencies.

• Create and maintain technical documentation (data contracts, pipeline specs, runbooks) and support operational handoffs.

What Should you have:

• 5+ years of hands-on experience in data engineering building production pipelines and data 5+ years of hands-on experience in data engineering building production pipelines and data platforms.

• Strong AWS experience: S3, Glue, Lambda, Step Functions, EMR (and/or ECS/Fargate), plus CloudWatch; solid grasp of IAM and encryption.

• Nice to have: AWS certification (Developer/Architect) or equivalent demonstrated expertise.

• Experience working in Agile teams; strong collaboration, communication, and stakeholder management skills.

• Experience with Databricks and lakehouse capabilities (e.g., Delta Lake, job/workflow orchestration, cluster tuning) is strongly preferred.

• Strong SQL skills including complex joins/window functions, data profiling, and performance tuning; understanding of