**What you’ll do** - **Build the data platform:** Design and deliver a scalable platform that serves as the primary engine for the Thanks Network. You’ll move us beyond operational databases into a well-modeled environment that supports both business intelligence and high-scale feature engineering. - **Own the models: **You won’t just move data; you will own the data science, technical implementation and performance of our ranking systems. You’ll be responsible for the models that determine how we prioritise, personalise and deliver content across the network. - **Build for real-time inference: **Own the end-to-end lifecycle of our models – from training and validation to real-time inference. You’ll ensure our ranking system is fast, reliable, and fed by high-quality, near-real-time data. - **Unlock model experimentation: **You will build the framework that allows us to run experiments on our ranking systems, ensuring we can accurately measure lift, attribution, and model success. - **Own pipelines & observability: **Build robust batch and near-real-time pipelines that are resilient and observable. You’ll ensure that the data feeding our models and experimentation frameworks is flawless. - **Enable self–serve analytics: **Design clean, trusted datasets and data marts that allow product, engineering, and commercial teams to answer their own questions without bottlenecks. - **Set the data direction:** Be opinionated about tooling, architecture, and trade-offs – helping define what to build, what to buy, and what to retire as our data needs evolve. - **Lead through expertise:** Act as the go-to expert for data across the business, influencing roadmaps and decisions through strong technical judgment rather than formal authority.

• Collaborate closely with data scientists, architects, and other stakeholders to understand and implement business requirements • Provide data engineering support for AI model development and deployment, ensuring data scientists have access to the data they need in the format they need it • Implement and optimize data transformations and ETL/ELT processes, using appropriate data engineering tools • Work with a variety of databases and data warehousing solutions to store and retrieve data efficiently • Implement monitoring, troubleshooting, and maintenance procedures for data pipelines to ensure the high quality of data and optimize performance • Participate in the creation and ongoing maintenance of documentation, including data dictionaries, data catalogues, data flow diagrams, and process documentation

Description and Requirements GG11.1 Role Value Proposition Data Engineer plays a critical role in data and analytics life cycle and significantly contributes to production grade data and analytics solutions. The role requires one to demonstrate Big Data, Engineering and Cloud expertise. This role mentors Data engineers. It is an individual contributor role, expected to solve wide ranging business problems, Experience 8-10+ years of relevant experience Education Bachelors degree in computer science, information technology or equivalent educational qualification Responsibilities • Design, build, and maintain robust ETL/ELT pipelines on cloud(Azure) or on-prem to collect, ingest and store large volumes of structured and unstructured data for batch/real time processing• Monitor, optimize, and troubleshoot data pipelines to ensure reliability, scalability, and performance• Ensure data processing, quality, security, and compliance guidelines, policies and standards are followed• Collaborate with multiple partners from Business, Technology, Operations and D&A capabilities (Data Governance, Data Quality, Data Modeling, Data Architecture, Data science, DevOps, BI & insights) • Mentor Data Engineers• Independently lead design, solutioning & estimations Technical Skills • SQL, Python/Scala• NoSql and distributed databases (Hbase, Cosmos DB)• ETL pipleine design and development; Solutioning and estimation• Big Data Frameworks : Apache Spark, Hadoop, Hive• Cloud platforms: Azure data factory, Eventhub, Azure functions, Synapse, Databricks• Datawarehouses, data marts, data lakes• Medallion architecture• Performance tuning, optimization, and data quality validation• Real-time and batch data processing , streaming pieplines with Spark• Communication skills, analytical skills, structured problem-solving skills, mentorship skills• Mentorship experience• Storytelling skills , Partner & Stakeholder engagement experience Good To have • DevOps practices: Git, AzureDevops, CI/CD pipelines• Unix shell scripting, Kafka,MongoDB, Nifi• Exposure to Gen AI technology and tools• Banking Financial Services and Insurance domain knowledge About MetLife Recognized on Fortune magazine's list of the "World's Most Admired Companies" and Fortune World's 25 Best Workplaces™, MetLife, through its subsidiaries and affiliates, is one of the world's leading financial services companies; providing insurance, annuities, employee benefits and asset management to individual and institutional customers. With operations in more than 40 markets, we hold leading positions in the United States, Latin America, Asia, Europe, and the Middle East. Our purpose is simple - to help our colleagues, customers, communities, and the world at large create a more confident future. United by purpose and guided by our core values - Win Together, Do the Right Thing, Deliver Impact Over Activity, and Think Ahead - we're inspired to transform the next century in financial services. At MetLife, it's #AllTogetherPossible . Join us! #BI-Hybrid

Role Description AI is only as smart as the data it consumes. We are seeking an AI Data Engineer to ensure our agent ecosystem is powered by high-quality, structured, and retrieval-ready data. You will serve as the primary bridge between our AI Center of Excellence and existing data teams, coordinating data readiness and architecting the custom integrations or interfaces needed to expose business data to AI tools. By building and managing reusable data pipelines and retrieval architectures, you ensure our AI agents can access the right information securely and performantly, working in lockstep with the AI Solutions Architect to bring high-value agents to life. Responsibilities - Custom Data Integrations: - Building and maintaining the custom connectors (APIs, ETL pipelines) required to extract data from core business systems for use in AI tools. - Working with system owners to unlock "siloed" data that is currently inaccessible to our AI ecosystem. - Data Pipeline & RAG Optimization: - Designing and maintaining the data pipelines that feed our AI knowledge base. - Optimizing "Retrieval-Augmented Generation" (RAG) performance by improving how documents are chunked, tagged, and indexed to reduce hallucinations. - Ensuring data freshness so agents never act on obsolete information. - Data Library Management: - Creating and maintaining the "AI Data Library" - a comprehensive technical map of where our enterprise data lives, its schema, and its owner. - Working with business teams to identify "Dark Data" (valuable data trapped in PDFs or desktops) and bringing it into the AI ecosystem. - Data Quality & Governance: - Implementing automated checks to monitor data quality and completeness. - Ensuring sensitive data (PII) is properly flagged and excluded from general AI access where appropriate. Qualifications - Demonstrated experience integrating data with Enterprise Search engines and AI agents, or RAG-based systems. - Practical experience preparing data specifically for consumption by Large Language Models and agentic orchestration tools. - 5+ years in data engineering, ETL development, or database management. - Strong experience building custom API connectors and data ingestion scripts. - Experience working with unstructured data (text, documents) and NLP concepts. - Strong proficiency in SQL and Python. Skills - Meticulous attention to detail—you care deeply about data cleanliness. - Understanding of enterprise knowledge management challenges. - Ability to audit data sources and identify gaps. - Strong communication skills to work with business owners on data access and cleanup. Benefits - Flexible work schedule - Remote-friendly environment - Comprehensive Insurance Coverage (Medical, Dental, Vision, Life) - Comprehensive PTO Structure (PTO, Sick Leave, Bereavement) - Global Parental Leave - Company issued equipment (Laptop, monitor, etc.) - 401k with match - Career Development/Pathing - EAP Program/Mental Health Advocacy - Supportive Work Culture Compensation Compensation & Pay Transparency: SAP Taulia believes that pay transparency contributes to an honest, supportive culture and is a vital part of our commitment to equity. The estimated annualized base salary range for this position is $165,000 - $185,000. This range represents a good faith estimate of the base salary at the time of posting. Actual compensation will be determined by a variety of factors, including (but not limited to) the candidate’s specific skills, depth of experience, qualifications, and geographic location. AI Statement At Taulia, we are embracing Artificial Intelligence to revolutionize our industry and empower our team. We believe AI is a fundamental tool for innovation, efficiency, and creative problem-solving. We are looking for forward-thinking individuals who share our excitement for the future and are eager to learn, experiment, and grow with us in an AI-driven landscape. If you have a curious, adaptive, and collaborative mindset, you will thrive here. Company Description Taulia is a fintech company that is part of the SAP group. More than 2 million businesses in over 150 countries collaborate on our network. Our customers include Airbus, AstraZeneca, and Nissan. We help them and their suppliers access cash when and where they need it most. Some of our customers have improved their working capital by more than $1 billion while making their supply chains more resilient and sustainable.