Principal Data Engineer

By clicking the “Apply” button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda’s Privacy Notice and Terms of Use. I further attest that all information I submit in my employment application is true to the best of my knowledge.

Job Description

Principal Data Engineer

About the Role

We are seeking a highly experienced Principal Data Engineer to lead the design, architecture, and implementation of enterprise-scale data platforms that power Takeda's R&D, Clinical, Regulatory, Commercial, and Enterprise Analytics ecosystems.

As a technical leader, you will drive the strategic direction of data engineering, define architecture standards, and deliver scalable, secure, and compliant data solutions using Databricks, AWS, PySpark, Delta Lake, and modern DataOps practices. You will partner with business stakeholders, solution architects, product owners, data scientists, and engineering teams to build reliable data products that accelerate innovation and data-driven decision making across Takeda.

This role requires deep expertise in modern cloud data platforms, distributed data processing, software engineering practices, and cross-functional leadership. You will mentor engineering teams, establish best practices, and ensure enterprise data solutions meet quality, scalability, security, and regulatory requirements.

Key Responsibilities

Data Architecture & Platform Leadership

• Lead architecture, design, and implementation of enterprise data platforms on Databricks and AWS.
• Define and govern enterprise data engineering standards, reference architectures, and reusable frameworks.
• Design scalable Lakehouse architectures using Delta Lake, Unity Catalog, Databricks Workflows, and AWS cloud services.
• Collaborate with enterprise architects and business stakeholders to translate business requirements into scalable technical solutions.
• Drive platform modernization initiatives, cloud migration programs, and data transformation roadmaps.
• Establish best practices for data modeling, metadata management, data lineage, and governance.
• Evaluate emerging technologies and recommend improvements to Takeda's data ecosystem.

Data Engineering & Solution Delivery

• Design and build high-performance batch and streaming (Optional) data pipelines using PySpark, Spark SQL, and Databricks.
• Architect ingestion frameworks supporting structured, semi-structured, and unstructured data from internal and external systems.
• Lead implementation of medallion architecture patterns (Bronze, Silver, Gold) to support trusted enterprise data products.
• Optimize large-scale data processing workloads to improve performance, reliability, and cost efficiency.
• Design and implement reusable ETL/ELT frameworks and accelerator components.
• Establish data contracts and engineering standards to ensure consistency and reliability across platforms.
• Ensure data solutions are scalable, maintainable, and aligned with enterprise architecture principles.

Cloud Engineering & AWS Platform Management

• Architect and implement cloud-native data solutions using AWS services including:
  • S3
  • IAM
  • Glue
  • Lambda
  • ECS/EKS
  • Step Functions
  • EventBridge
  • CloudWatch
  • Secrets Manager
  • KMS
  • Redshift
• Design secure multi-account architectures and governance models.
• Establish infrastructure automation practices using Terraform, CloudFormation, or AWS CDK.
• Drive optimization of cloud resources through cost management, workload tuning, and automation.
• Partner with cloud platform teams to ensure operational excellence and security compliance.

DataOps, CI/CD & Engineering Excellence

• Lead adoption of software engineering best practices across data engineering teams.
• Design and implement CI/CD pipelines for data platforms using GitHub Actions, DevOps, GitLab CI, or Jenkins.
• Establish automated deployment frameworks across Development, Test, Validation, and Production environments.
• Implement unit testing, integration testing, regression testing, and automated quality gates.
• Drive code quality initiatives including:
  • Peer reviews
  • Static code analysis
  • Test automation
  • Release management
  • Version control strategies
• Standardize engineering practices to improve delivery velocity and platform reliability.
• Promote Infrastructure as Code and automated environment provisioning.

Data Quality, Governance & Compliance

• Establish enterprise data quality frameworks and monitoring capabilities.
• Implement end-to-end data lineage, metadata management, and observability solutions.
• Collaborate with governance, security, quality, and compliance teams to ensure adherence to corporate standards.
• Support implementation of:
  • Data governance policies
  • Role-based access controls
  • Data retention policies
  • Auditability requirements
• Ensure compliance with:
  • GxP requirements
  • HIPAA
• Promote secure handling of sensitive healthcare and research data.

Performance Optimization & Reliability Engineering

• Establish platform observability using monitoring, logging, and alerting solutions.
• Define service-level objectives and operational metrics for critical data platforms.
• Lead root-cause analysis and resolution of complex production issues.
• Implement resiliency, disaster recovery, and business continuity strategies.
• Continuously improve platform performance, stability, and operational efficiency.

Leadership, Mentoring & Cross-Functional Collaboration

• Provide technical leadership and guidance to data engineers across multiple programs and delivery teams.
• Lead architectural reviews, design discussions, and engineering governance forums.
• Mentor engineers in Databricks, Spark, AWS, DataOps, and software engineering best practices.
• Partner with:
  • Product Owners
  • Business Stakeholders
  • Data Scientists
  • Cloud Engineering Teams
  • Security Teams
  • Quality and Compliance Teams
• Influence strategic data platform decisions and long-term technology roadmaps.
• Drive delivery excellence through collaboration, coaching, and continuous improvement.

Required Qualifications

• Bachelor’s or master’s degree in computer science, Engineering, Information Systems, or related field.
• 12+ years of experience in Data Engineering, Big Data, Data Platforms, or Cloud Engineering.
• 4 to 5 years of experience architecting enterprise-scale data solutions on Databricks / AWS.
• Deep expertise in:
  • Databricks
  • Delta Lake
  • Unity Catalog
  • Spark
  • PySpark
  • SQL
• Strong AWS experience across compute, storage, security, and monitoring services.
• Advanced Python development experience and software engineering practices.
• Proven experience building large-scale enterprise data pipelines and Lakehouse architectures.
• Expertise in CI/CD implementation and Git-based development practices.
• Experience implementing automated unit testing and quality assurance frameworks.
• Strong understanding of distributed systems and performance optimization.
• Experience with Infrastructure as Code using Terraform or CloudFormation, or AWS CDK.
• Excellent communication, stakeholder management, and leadership skills.

Preferred Qualifications

• Life Sciences, Pharmaceutical, Healthcare, or Clinical data domain experience.
• Experience supporting GxP-regulated environments.
• Knowledge of:
  • Clinical Trial Data
  • Regulatory Data
  • Pharmacovigilance
  • Real World Data (RWD/RWE)
  • Omics and Research Data
• Experience with ML/AI data platforms and MLOps foundations.
• Exposure to streaming technologies such as Kafka, Kinesis, or Event Hubs.
• Databricks Certified Data Engineer Professional.
• AWS Solutions Architect Professional.
• AWS Data Analytics Specialty or equivalent certifications.

What Success Looks Like (First 12 Months)

• Data delivery timelines are significantly reduced through reusable frameworks, automation, and CI/CD.
• Data pipelines achieve high reliability, observability, and operational excellence.
• Engineering teams consistently follow software engineering, testing, and deployment best practices.
• Cloud infrastructure and Databricks environments are optimized for performance, scalability, and cost.
• Data products meet quality, governance, and compliance expectations across R&D and enterprise functions.
• Multiple teams successfully adopt reusable engineering accelerators developed under your leadership.
• Stakeholders recognize the data platform as a strategic enabler for innovation and business outcomes.

Locations

Worker Type

Worker Sub-Type

Time Type