Solution Architect, Data Platform
- Thomson Reuters
- 16 - 21 years
- Bengaluru
- 5 months ago
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Job Description
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 Solution Architect- Data Platform, you will:
Lead Architecture Design: Architect and Lead Data Platform Evolution: Spear head 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. Explicitly design and evolve the data platform to robustly support advanced analytics and Artificial Intelligence/Machine Learning (AI/ML) based services, enabling the creation of innovative AI-driven applications.
Technical Standards and Best Practices:
- Research and recommend technical standards, ensuring the architecture aligns with overall technology and product strategy.
- Be hands-on in implementing core components reusable across applications. Establish architectural standards and best practices for MLOps workflows, including automated model training, versioning, A/B testing, and continuous integration/deployment of AI models within the data platform infrastructure.
- 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 for transformations, and ensuring efficient data processing and quality. This also involves developing reusable patterns and integrations of AI services into the data ecosystem.
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.
- 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.
- 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 Solution Architect Data Platform if your background includes:
- Educational Background: Bachelor's degree in information technology.
- Experience: 16+ 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 soft ware design,
- Microservices architecture, Data Warehousing, and proficiency in Snowflak e. Broad knowledge and experience with Cloud-native software design, Microservices architecture, Data Warehousing, proficiency in Snowflake, and a proven ability to design data architectures that specifically cater to the needs of Artificial Intelligence and Machine Learning (AI/ML) workloads, including MLOps principles. Collaborate with data scientists and AI/ML practitioners to architect and integrate AI-powered services and solutions within the data platform, enabling advanced analytics, machine learning, and intelligent automation capabilities for business stakeholders. Develop frameworks, patterns, and reusable components to support the operationalization and scaling of AI/ML models leveraging the organizations cloud data stack.
- Solid understanding of the end-to-end AI/ML lifecycle from data preparation and feature engineering to model training, deployment, and monitoring. Demonstrated ability to stay current with AI trends, concepts, and emerging technologies, and apply them to enhance data platform capabilities and solutions.
- 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 with a strong focus on supporting data readiness for AI/ML. This includes experience with various ingestion patterns (batch, streaming, CDC) and cloud-native AI/ML services.
- 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, effectively balancing complex technical decisions with user needs and overarching business constraints
- Programming Skills: Strong programming skills in languages such as Python or Java or data manipulation, automation, and API development.
- Regulatory Awareness and Security Acumen: Data Governance and Security Acumen Deep understanding and practical experience in designing and implementing solutions compliant with robust data governance principles, data security best practices (e.g., encryption, access controls, masking), and relevant privacy regulations (e.g., GDPR, CCPA).
- Containerization and Orchestration: Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
Job Classification
Industry: Advertising & MarketingFunctional Area / Department: Engineering - Software & QARole Category: Software DevelopmentRole: Solution ArchitectEmployement Type: Full timeContact Details:
Company: Thomson ReutersLocation(s): Bengaluru+ View Contact
Keyskills: snowflake python data warehousing microservices java kubernetes data manipulation software design data architecture machine learning data preparation artificial intelligence docker nosql data modeling data vault api aws etl data lake
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