About the role:

The Data Engineering Engineer collaborates with a multidisciplinary Agile team to build high-quality data pipelines that drive analytic solutions. This role focuses on generating insights from connected data, enabling advanced data-driven decision-making capabilities.

How you will contribute:

• Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals
• Define data requirements, gather and mine large-scale structured and unstructured data, and validate data using various tools in the Big Data Environment
• Support?Data Scientists?in data sourcing and preparation to visualize data and synthesize insights of commercial value
• Collaborates with analytics and business teams to improve data models that feed business intelligence tools and dashboards, increasing data accessibility and fostering data-driven decision making across the organization.
• Implement processes and systems to ensure data reconciliation, monitor data quality, and ensure production data is accurate and available for key stakeholders, downstream systems, digital products, and business processes.
• Write unit, integration, and performance test scripts, and contribute to data engineering documentation.
• Perform data analysis to troubleshoot and resolve data-related issues.
• Work closely with AGILE SCRUM Teams, specifically frontend and backend engineers, product managers, scrum masters, quality engineers to deliver integrated and scalable data products.
• Collaborate with enterprise teams, including Enterprise Architecture, Security, and Enterprise Data Backbone Engineering, to design and develop data integration patterns and models supporting various digital products.
• Partner with DevOps and the Cloud Center of Excellence to deploy data pipeline solutions in the Takeda AWS environments, meeting security and performance standards.
• Support and align with Data Trustees, Data Stewards, and Master Data Management functions following Data Governance principles.
• Implement statistical data quality procedures on new data sources by applying rigorous iterative data analytics

Skills and qualifications:

Education Requirements:

• Bachelor's or Master's degree in Computer Science, Data Science, Information Design, or a related field

Experience Requirements:

• 3+ years of proven experience as a Data Visualization Engineer,?Data Engineer,?Data Analyst, or a similar role.
• Proficiency in data visualization tools such as Power BI (required) or?QlikView (nice to have) and familiarity in programming languages like Python or R for data manipulation and analysis.
• Advanced knowledge of SQL, executing complex queries and managing databases effectively.
• Strong expertise in data integration, data modeling, and modern database technologies (GraphQL, SQL, No-SQL, python, pySpark) and AWS cloud technologies (e.g., DMS, Lambda, Databricks, SQS, Step Functions, Data Streaming).
• Understanding of data architecture and models (Data lake, Erwin).
• Proficient in programming languages such as Python, Scala, Java, and C++, tailored for scalable data engineering solutions.
• Experienced in system integration, ensuring seamless data flow and functionality across various platforms.
• Strong analytical and problem-solving skills with the ability to interpret complex data and translate it into actionable insights?for decision making.
• Good communication and presentation skills, with the ability to effectively convey technical information to both technical and non-technical stakeholders.
• Detail-oriented with a strong focus on data accuracy and integrity.
• Knowledge of testing tools and practices (unit testing, integration testing, E2E testing).
• Knowledge of SDLC?procedures,?version control (e.g., Git, GitHub, GitLab) and familiarity with CI/CD pipelines (e.g., Jenkins, GitHub Actions).
• Proficient working in AGILE SCRUM Teams and using JIRA and Confluence.