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 Takedas data transformation journey through strong engineering practices collaboration and scalable platform-aligned development.

RESPONSIBILITIES:

• Design develop test and maintain scalable data pipelines and integrations usingDatabricks PySpark and SQL.
• Build datasets optimized for analytics BI and downstream consumption while ensuring data quality reconciliation and production reliability.
• Work within establisheddata frameworks design patterns and reusable componentscreated 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:

• Bachelors or Masters degree in Computer Science Engineering Information Systems or related field.
• 5 years of experiencein data engineering data warehousing or large-scale data platform development.
• Strong hands-on experience withDatabricksand distributed data processing.
• Strong hands-on experience withPySparkfor pipeline development and transformation of large datasets.
• Strong hands-on experience withSQL 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 asAWS or Azure.
• Experience working in agile cross-functional engineering environments.

KEY SKILLS AND COMPETENCIES:

• Strong proficiency inPySpark and SQL;Python alone is not sufficientfor this role.
• Strong understanding of distributed data processing performance optimization and scalable pipeline design.
• Ability to work effectively within predefinedpatterns 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 asSpark Structured StreamingorKafka.

Experience with orchestration and workflow tools in enterprise data environments.

Experience with Infrastructure as Code preferablyTerraform.

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.