Data Architect, Data

Are you ready to play a key role in transforming Thomson Reuters into a truly data-driven company? Join our Data & Analytics (D&A) function and be part of the strategic ambition to build, embed, and mature a data-driven culture across the entire organization.

The Data Architecture organization within the Data and Analytics division is responsible for designing and implementing a unified data strategy that enables the efficient, secure, and governed use of data across the organization. We aim to create a trusted and customer-centric data ecosystem, built on a foundation of data quality, security, and openness, and guided by the Thomson Reuters Trust Principles. Our team is dedicated to developing innovative data solutions that drive business value while upholding the highest standards of data management and ethics.

**About the Role**

In this opportunity as a **Data Architect,**you will:

- ** Lead Architecture Design**:Architect and Lead Data Platform Evolution: Spearhead the conceptual, logical, and physical architecture design for our enterprise Data Platform (encompassing areas like our data lake, data warehouse, streaming services, and master data management systems). You will define and enforce data modeling standards, data flow patterns, and integration strategies to serve a diverse audience from data engineers to AI/ML practitioners and BI analysts.
- ** Technical Standards and Best Practices**:

- Hands-on Prototyping and Framework Development: While a strategic role, maintain a hands-on approach by designing and implementing proof-of-concepts and core reusable components/frameworks for the data platform. This includes developing best practices and templates for data pipelines, particularly leveraging dbt for transformations, and ensuring efficient data processing and quality.
- Champion Data Ingestion Strategies: Design and oversee the implementation of robust, scalable, and automated cloud data ingestion pipelines from a variety of sources (e.g., APIs, databases, streaming feeds) into our AWS-based data platform, utilizing services such as AWS Glue, Kinesis, Lambda, S3, and potentially third-party ETL/ELT tools.
- Design and optimize solutions utilizing our core cloud data stack, including deep expertise in Snowflake (e.g., architecture, performance tuning, security, data sharing, Snowpipe, Streams, Tasks) and a broad range of AWS data services (e.g., S3, Glue, EMR, Kinesis, Lambda, Redshift, DynamoDB, Athena, Step Functions, MWAA/Managed Airflow) to build and automate end-to-end analytics and data science workflows.
- ** Data-Driven Decision-Making**: Make quick and effective data-driven decisions, demonstrating strong problem-solving and analytical skills. Align strategies with company goals.
- ** Stakeholder Collaboration**:Collaborate closely with external and internal stakeholders, including business teams and product managers. Define roadmaps, understand functional requirements, and lead the team through the end-to-end development process.
- ** Team Collaboration**: Work in a collaborative team-oriented environment, sharing information, diverse ideas, and partnering with cross-functional and remote teams.
- ** Quality and Continuous Improvement**:Focus on quality, continuous improvement, and technical standards. Keep service focus on reliability, performance, and scalability while adhering to industry best practices.
- ** Technology Advancement**: Continuously update yourself with next-generation technology and development tools. Contribute to process development practices.

**About You**
- You're a fit for the role of **Data Architect, Data Platform** if your background includes:

- ** Educational Background**: Bachelor's degree in information technology.
- ** Experience**: 10 + years of IT experience with at least 5 years in a lead design or architectural capacity.
- ** Technical Expertise**: Broad knowledge and experience with Cloud-native software design, Microservices architecture, Data Warehousing, and proficiency in Snowflake.
- ** Cloud and Data Skills**: Experience with building and automating end-to-end analytics pipelines on AWS, familiarity with NoSQL databases. Data Pipeline and Ingestion Mastery: Extensive experience in designing, building, and automating robust and scalable cloud data ingestion frameworks and end-to-end data pipelines on AWS. This includes experience with various ingestion patterns (batch, streaming, CDC) and tools.
- ** Data Modeling**: Proficient with concepts of data modeling and data development lifecycle. Advanced Data Modeling: Demonstrable expertise in designing and implementing various data models (e.g., relational, dimensional, Data Vault, NoSQL schemas) for transactional, analytical, and operational workloads. Strong understanding of the data development lifecycle, from requirements gathering to deployment and maintenance.
- ** Leadership**: Proven ability to lead architectural discussions, influence technical direction, and mentor data engineers,