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: 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 up-to-date 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: IND - Bengaluru

Worker Type: Employee

Worker Sub-Type: Regular

Time Type: Full time

📌 Principal Data Engineer (Bengaluru)
🏢 Takeda
📍 Bengaluru