Data Engineer

Overview

Build and maintain scalable data pipelines and datasets that support analytics, reporting, downstream business systems, and Takeda’s data transformation journey. Develop Databricks data solutions using established engineering patterns, reusable frameworks, and enterprise standards, with reliable, high-quality, and performant data delivery across batch and, where relevant, streaming use cases.

What you'll do

• Design, develop, test, and maintain scalable data pipelines and integrations using Databricks, PySpark, and SQL.
• Build datasets optimized for analytics, BI, and downstream consumption while ensuring data quality, reconciliation, and production reliability.
• Work within established data frameworks, design patterns, and reusable components created by other engineering teams.
• Read, understand, troubleshoot, and extend existing codebases and pipeline logic in line with engineering standards.
• Collaborate with analytics, product, and business teams to support data models and data products for enterprise use cases.
• Contribute to unit, integration, and performance testing, documentation, and engineering best practices.
• Partner with platform, architecture, security, and DevOps teams to deploy and support pipeline solutions in cloud environments.
• Troubleshoot data and pipeline issues and drive continuous improvement in performance, scalability, and maintainability.

What you'll need

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or related field.
• 5+ years of experience in data engineering, data warehousing, or large-scale data platform development.
• Strong hands-on experience with Databricks and distributed data processing.
• Strong hands-on experience with PySpark for pipeline development and transformation of large datasets.
• Strong hands-on experience with SQL, including joins, aggregations, optimization, and analytical data processing.
• Experience building and maintaining data pipelines for batch processing; exposure to streaming is a plus.
• Experience working with existing enterprise frameworks, shared libraries, and engineering standards.
• Experience reading, understanding, debugging, and enhancing existing code developed by other teams.
• Experience with cloud data platforms such as AWS or Azure.
• Experience working in agile, cross-functional engineering environments.
• Strong proficiency in PySpark and SQL; Python alone is not sufficient for this role.
• Strong understanding of distributed data processing, performance optimization, and scalable pipeline design.
• Ability to work effectively within predefined patterns, frameworks, and architectural guardrails.
• Strong code reading and code comprehension skills across shared enterprise codebases.
• Good understanding of data modeling, schema design, and data quality controls.
• Strong engineering discipline in testing, version control, documentation, and maintainable development.
• Strong problem-solving skills and ability to troubleshoot production data issues.
• Effective communication and collaboration with technical and non-technical stakeholders.

Nice to have

• Experience with streaming technologies such as Spark Structured Streaming or Kafka.
• Experience with orchestration and workflow tools in enterprise data environments.
• Experience with Infrastructure as Code, preferably Terraform.
• Experience designing and developing API-based integrations.
• Databricks Certified Data Engineer Associate / Professional certification.
• AWS or Azure Data Engineering certification.

Details

• Location: Bengaluru, India.
• Employment type: Full time; worker type Employee; worker sub-type Regular.
• Supervision scope: 0–3 direct and 0–3 indirect employees, and 0–3 direct and 0–3 indirect non-employees.
• Access to transportation is required to attend meetings.
• Ability to fly to meetings regionally and globally.
• Physical demands: N/A.