Data Engineer

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PRIMARY OBJECTIVES: 

• Build and maintain scalable data pipelines and datasets that support analytics, reporting, and downstream business systems.
• Develop data solutions on Databricks using established engineering patterns, reusable frameworks, and enterprise standards.
• Ensure reliable, high-quality, and performant data delivery across batch and, where relevant, streaming use cases.
• Support Takeda’s data transformation journey through strong engineering practices, collaboration, and scalable platform-aligned development.

RESPONSIBILITIES: 

• 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.

SCOPE OF SUPERVISION:

NUMBER SUPERVISED WORKERS

Direct

Indirect

Employees

0-3

0-3

Non-Employees

0-3

0-3

EDUCATION AND EXPERIENCE:  

• 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.

KEY SKILLS AND COMPETENCIES:  

• 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.

·         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.

LICENSES/CERTIFICATIONS:

• Preferred - Databricks Certified Data Engineer Associate / Professional
• Preferred - AWS or Azure Data Engineering certification

PHYSICAL DEMANDS: 

·         N/A

TRAVEL REQUIREMENTS:

·         Access to transportation to attend meetings.

·         Ability to fly to meetings regionally and globally.