Senior Data engineer at Takeda in Bengaluru, Karnataka, India

Senior Data engineer Location: IND - Bengaluru 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 The Future Begins Here At Takeda, we are leading digital evolution and global transformation. By building innovative solutions and future-ready capabilities, we are meeting the need of patients, our people, and the planet. Bengaluru, the city, which is India’s epicenter of Innovation, has been selected to be home to Takeda’s recently launched Innovation Capability Center. We invite you to join our digital transformation journey. In this role, you will have the opportunity to boost your skills and become the heart of an innovative engine that is contributing to global impact and improvement. At Takeda’s ICC we Unite in Diversity Takeda is committed to creating an inclusive and collaborative workplace, where individuals are recognized for their backgrounds and abilities they bring to our company. We are continuously improving our collaborators journey in Takeda, and we welcome applications from all qualified candidates. Here, you will feel welcomed, respected, and valued as an important contributor to our diverse team About the role: As a Senior Data Engineer, you will play a hands-on technical role in designing, building, and optimizing scalable enterprise data architectures and high-quality data pipelines that deliver trusted, actionable insights across the business. You will drive strong engineering standards through technical ownership, mentoring, and collaboration, ensuring secure, reliable, and well-governed data assets that power business intelligence, analytics, and AI-driven decision-making. You will contribute across the end-to-end data lifecycle, ensuring production data remains accurate, timely, and enterprise ready. By embedding data quality, governance, reconciliation, and performance optimization into pipeline design, you will enable scalable analytics and advanced AI/ML use cases while helping teams adopt modern engineering best practices. You will partner closely with Design & Engineering Leads, Delivery Leads, Business Intelligence, Data Science, AI, and other global PDT DD&T stakeholders across India, Europe, and the United States to shape modern data capabilities, support delivery excellence, and advance enterprise-wide insight generation. This role reports to the PDT Delivery Lead, ICC India, and is aligned with the Data Chapter as a senior individual contributor with strong technical influence. How you will contribute: Engineering & Design Lead the end-to-end design, and implementation of scalable batch, micro-batch, and streaming data platforms aligned to enterprise data strategy Define, implement, and enforce data engineering standards, design patterns, and governance controls to ensure secure, reliable, and production-ready data assets. Shape storage, compute, and processing architectures across ingestion, transformation, serving, and observability layers, ensuring high availability, resiliency, and recoverability. Establish and own engineering quality strategy, including testing frameworks, release readiness, and continuous improvement of platform reliability and performance. Databricks, Spark & Performance Engineering Provide deep technical leadership in Spark distributed computing and Databricks Lakehouse architecture, guiding solution design and engineering best practices. Lead large-scale performance optimization, including cluster configuration, autoscaling, caching, storage formats, and workload tuning for cost and efficiency. Diagnose and resolve complex platform or workload issues using Spark UI, Ganglia, and Databricks observability metrics, driving measurable improvements in stability and throughput. Drive pipeline redesign and storage optimization strategies (Delta, Parquet, partitioning, Z-ordering) to balance scalability, performance, and cloud cost. Implement robust observability, error handling, retry, and checkpointing mechanisms, and define SLOs/SLAs to ensure consistent production reliability. Technical Mentorship & Engineering Excellence Act as a senior technical contributor within the ICC’s strategic capability, leading by example through hands-on delivery, sound engineering judgment, and a strong focus on quality. Translate enterprise architectural direction and global priorities into clear technical approaches, implementation guidance, and measurable engineering outcomes. Mentor engineers on distributed data processing, Databricks engineering patterns, ETL design, and production readiness, helping elevate overall team capability. Lead design walkthroughs, reviews, and code quality practices, ensuring scalable, maintainable, and secure implementations. Promote a culture of agile delivery, reuse, automation, and operational stability across the data engineering lifecycle through influence, collaboration, and technical stewardship. Global Collaboration & Stakeholder Engagement Partner closely with geographically distributed business product owners, architects, and platform teams to clarify requirements, constraints, and acceptance criteria. Communicate technical recommendations, trade-offs, and architectural decisions clearly to both technical and non-technical stakeholders. Collaborate across data science, AI/ML, analytics, cloud, security, and integration teams to enable enterprise-wide insight generation and AI adoption. Contribute to global forums, design discussions, and strategic initiatives as a senior technical voice, helping ensure alignment with enterprise standards and delivery outcomes. Data Governance, Quality & Cost Stewardship Embed data quality, reconciliation, lineage, and governance controls into pipeline and platform design, including secure access models and metadata management Leverage governed data platform capabilities to ensure trusted, compliant, and discoverable enterprise data. Drive cloud cost optimization strategies across storage, compute, and workload design while maintaining performance and scalability. Ensure production data accuracy, timeliness, and reliability for downstream analytics, reporting, and AI-driven decision-making. Minimum Requirements/Qualifications: Bachelor's degree in Engineering, Computer Science, Data Science, or related field 8+ years of experience in software development, data engineering, ETL, and analytics reporting, with demonstrated experience in mentoring engineers and leading technical delivery for complex initiatives Expert in building and maintaining data and system integrations using dimensional data modelling and optimized ETL pipelines. Advanced experience with modern data architectures and frameworks (data mesh, data fabric, data products) and scalable multi-source data integration across structured and unstructured data. Proven track record of designing and implementing complex enterprise scale data solutions Strong proficiency in Python, SQL, and PySpark, with hands-on experience in Spark and distributed data processing, including real-time pipelines using Spark Structured Streaming. Experience with AWS cloud services (e.g., Lambda, DMS, Step Functions, S3, EventBridge, CloudWatch, Aurora RDS) and DevOps/CI practices, including automated deployments via GitHub Actions. Deep understanding of database architecture, data modeling, relational databases, data lakes, data warehouses, and Databricks/Delta Lakehouse. Experience extracting, transforming, and consolidating multi-source enterprise data into governed, analytics-ready platforms supporting BI and visualization. Familiarity with code repositories and version control (GitHub, GitLab, or similar). Strong experience in code reviews, performance tuning, scalability, and maintainability of data engineering solutions. Ability to optimize AWS/Databricks cloud costs and ensure efficient infrastructure utilization. Experience with Databricks Unity Catalog for centralized governance, lineage, and secure access control. Excellent communication, storytelling, and stakeholder engagement across cross-functional and global teams. Strong organizational, troubleshooting, and problem-solving capabilities with the ability to manage multiple concurrent initiatives in fast-paced environments. Experience working in globally distributed delivery models and championing engineering best practices across teams. Preferred requirements: Master's degree in engineering specialized in Computer Science or related field Demonstrated understanding and experience using: Knowledge in CDK Experience in IICS Data Integration tool Job orchestration tools like Tidal/Airflow/ or similar Knowledge on NoSQL ETL tools like DataStage, Ab Initio, Talend Databricks Certified Data Engineer Professional AWS Certified Data Engineer – Associate BENEFITS: It is our priority to provide competitive compensation and a benefit package that bridges your personal life with your professional career. Amongst our benefits are: Competitive Salary + Performance Annual Bonus Flexible work environment, including hybrid working Comprehensive Healthcare Insurance Plans for self, spouse, and children Group Term Life Insurance and Group Accident Insurance programs Employee Assistance Program Broad Variety of learning platforms Diversity, Equity, and Inclusion Programs Reimbursements – Home Internet & Mobile Phone Employee Referral Program Leaves – Paternity Leave (4 Weeks) , Maternity Leave (up to 26 weeks), Bereavement Leave (5 calendar days) ABOUT ICC IN TAKEDA: Takeda is leading a digital revolution. We’re not just transforming our company; we’re improving the lives of millions of patients who rely on our medicines every day. As an organization, we are committed to our cloud-driven business transformation and believe the ICCs are the catalysts of change for our global organization. #Li-Hybrid Locations IND - Bengaluru Worker Type Employee Worker Sub-Type Regular Time Type Full time