Role Description We are actively hiring and scheduling interviews this week for a fully remote Work From Home position. Immediate hiring – secure your spot and get hired. Entry level position for the applicants and full training provided. This is a legitimate opportunity with full training provided and guidance to obtain your Life & Health Insurance license. No prior experience required. We are looking for motivated U.S. residents ready to grow in a long-term remote career. - Communicate professionally with clients - Provide information and guidance - Follow a structured system - Maintain consistent performance Qualifications - Strong communication skills - Reliable internet connection - Self-motivated and coachable - Must be a U.S. resident - Willingness to obtain a Life & Health Insurance license (assistance provided) Benefits - 100% Remote - Full training program - Licensing guidance and support - Advancement opportunities - Supportive leadership team Company Description

Role Description Oxford's client, a global leader in the biopharma industry, is seeking a Data Engineer to support a growing workload within their innovation team. This role will be instrumental in building and maintaining scalable data pipelines, delivering high-quality, production-ready datasets to cross-functional teams including IT and AI. - Design, build, and maintain robust data pipelines within a Snowflake environment - Transform raw global data (including datasets from China and other international regions) into clean, structured, and production-ready outputs - Deliver polished datasets to downstream teams including IT and AI/ML teams - Collaborate closely with AI teams to support data-driven models and workflow automation initiatives - Utilize Databricks to support data processing, analytics, and pipeline orchestration - Structure and optimize data models to ensure usability, scalability, and performance - Partner with IT teams involved in data mining and analytics to ensure seamless data access and usability - Help reduce backlog by supporting and scaling existing data engineering efforts - Ensure data processes align with industry compliance standards and business requirements Qualifications - Exposure to AI/ML workflows, including automation and data pipeline integration - Experience in the biopharma, life sciences, or regulated industries - Proven experience as a Data Engineer in a complex, data-driven environment - Strong hands-on experience with Snowflake - Experience building and maintaining end-to-end data pipelines - Proficiency with Databricks and modern data engineering tools - Solid understanding of data modeling and structuring techniques - Experience working with both structured and semi-structured data - Ability to work independently as a self-starter in a fast-paced environment - Experience working with global datasets and distributed teams Preferred Qualifications - Familiarity with data compliance, governance, and regulatory requirements - Experience supporting or collaborating with AI/ML teams

Role Description Our client is looking for an AI / Data Engineer to design and deliver data platforms, pipelines and AI-enabled solutions for our clients. This is a hands-on consulting and delivery role for someone who can take ownership of ambiguous client problems, shape a practical technical approach, and deliver robust solutions across data engineering, data architecture and AI innovation. You will work with the company’s colleagues, client stakeholders and external technical teams to acquire, structure, transform and expose data through analytics, applications, APIs and AI-enabled experiences. Duties & Responsibilities - Own the delivery of client data and AI engineering work from discovery through design, implementation, testing and deployment. - Work with client stakeholders to understand business problems, clarify requirements and translate them into practical technical solutions. - Design and build data ingestion pipelines from APIs, third-party systems, files, online sources and operational platforms. - Develop scalable warehouse and transformation layers that convert raw data into trusted, reusable client data products. - Apply sound data architecture, modelling, quality, lineage and governance practices. - Identify and implement opportunities to use AI, automation, retrieval and agent-based workflows within client solutions. - Build or support APIs, data access layers and application integrations that make data products usable by reporting tools, software applications and AI experiences. - Contribute reusable technical patterns, documentation and engineering standards across the company. Qualifications - Ownership and Client Problem-Solving: You take ownership of outcomes rather than waiting for fully defined requirements. You are comfortable working through ambiguity, asking the right questions, identifying gaps and helping clients move from a business problem to a practical solution. - AI-First Mindset: You are interested in how AI can improve data collection, enrichment, research, automation, retrieval and decision support. You do not need to be an AI researcher, but you should be comfortable evaluating where LLMs, agents and AI-enabled workflows can create practical value. - Data Engineering and Warehousing Capability: You have strong hands-on experience designing and delivering modern data pipelines and warehouse solutions. You understand ingestion patterns, orchestration, data modelling, transformation layers, quality controls, observability, lineage, performance and security. - Collaborative Delivery Mindset: You work well in a consulting environment, communicate clearly with technical and non-technical stakeholders, and are comfortable sharing ideas, challenging assumptions and working closely with client and internal delivery teams. Requirements - Approximately five to ten years of relevant experience in data engineering, analytics engineering, data platform delivery or related technical consulting roles. - Practical experience with several of the following: - Modern cloud data warehouses such as Snowflake, BigQuery, Redshift, Databricks or Synapse. - SQL, data modelling and transformation frameworks such as dbt. - Cloud platforms, particularly AWS, Azure or GCP. - API integration, external data ingestion and pipeline orchestration. - Data quality, monitoring, observability and governance practices. - Software engineering practices including Git, automated testing and CI/CD. - Building data products for analytics, reporting, APIs or software applications. - Experience with AI orchestration frameworks, LLM tool calling, retrieval workflows, vector search, agent-based systems or related AI technologies. Success in This Role Success will mean delivering reliable, scalable and commercially useful data and AI solutions for the clients. You will combine sound engineering discipline with strong client problem-solving: understanding the real need, designing the right solution, delivering it effectively and helping clients derive measurable value from their data. Kindly regard your application as unsuccessful if you have not heard from the agency within 2 weeks.

• Partner with the CTO and leadership to set the Intelligence strategy and roadmap; own the execution. • Build, hire, and develop the Intelligence team — set the bar for craft, shape the operating cadence, and build the collaboration patterns with product, platform, and engineering. • Stand up the canonical data substrate: schema discipline, tenancy isolation, data contracts, lineage, and governance that AI/ML workloads run cleanly against. • Stand up the ML and AI platform: model lifecycle, feature store, vector store, training and serving infrastructure, and MLOps practice. • Lead the learning and reasoning capabilities of the platform: RAG architectures, agentic data systems, knowledge graphs, and the patterns that let Stratus's data compound into platform intelligence. • Develop and drive evaluation frameworks measuring model quality, agent reliability, drift, and platform effectiveness — make AI workloads observable to engineering, product, and customer success. • Drive the build-vs-buy posture for the AI/ML stack; set production readiness standards for AI workloads in close collaboration with the platform team. • Partner with product on the AI use case portfolio; engage directly with customers when needed to ground Intelligence decisions in real workflow problems.