FDE-Data Engineer (Forward Deployed)

Location: Remote | Type: Contract

About Newpage Solutions

Newpage Solutions is a global digital health innovation company helping people live longer, healthier lives. We partner with life sciences organisations which include, pharmaceutical, biotech and healthcare leaders, to build transformative AI and data driven technologies addressing real-world health challenges.

From strategy and research to UX design and agile development, we deliver and validate impactful solutions using lean, human-centered practices.

We are proud to be a ‘Great Place to Work®’ certified company for the last three consecutive years. We also hold a top Glassdoor rating and are named among the "Top 50 Most Promising Healthcare Solution Providers" by CIOReview. As an organisation, we foster creativity, continuous learning and inclusivity, creating an environment where bold ideas thrive and make a measurable difference in people’s lives.

Your Mission

We are seeking a Senior Data Engineer who will design and build data integration, storage systems, and data infrastructure that enable analytics and AI-powered applications. In this role, you will master the art of gaining access to enterprise data and making it usable — integrating multiple data sources, cleaning messy real-world datasets, and building robust pipelines that downstream consumers depend on. You will integrate AI tools strategically into your development workflow, reviewing AI-generated code with the same rigour as human code and never shipping code you don't fully understand.

In this forward-deployed role, you will be embedded directly with business units including Commercial, Manufacturing, and R&D. You will navigate ambiguous data landscapes independently — discovering data requirements through observation and stakeholder immersion, building working data pipelines rapidly, and iterating based on real consumer feedback. As part of the discovery-to-scale pipeline, you will identify recurring data patterns in your work and propose candidates for generalization, collaborating with Platform Engineers to validate reusable data solutions.

This role encompasses large features and technical initiatives, including leading small projects. You will do self-directed work while seeking input on strategic decisions, shape team practices, and mentor junior engineers. You will be comfortable navigating high complexity and ambiguity — turning undefined data problems into clear, buildable solutions.

What You’ll Do

Responsibilities:

• Business: Apply deep domain knowledge of commercial operations to data solutions. Bridge business and technology conversations fluently, speaking the domain language naturally. Shadow operations to build understanding of how data flows through commercial processes and make better technical decisions by understanding broader business context.
• Delivery: Deliver working data solutions rapidly — days not weeks. Integrate multiple data sources independently, clean, messy datasets, handle inconsistent formats and missing values, and document data lineage. Use prototypes to build stakeholder trust, know when to stop prototyping and start productionising, and balance speed with appropriate quality.
• People: Facilitate collaboration across the team, resolve minor conflicts before they escalate, and enable others to succeed. Present complex data topics clearly to any audience. Translate between technical and business language fluidly and write compelling proposals and specifications.
• AI-Augmented Development: Integrate AI tools strategically into your development workflow and that of the team. Review AI-generated code with the same rigour as human code and never ship code you don't fully understand. Develop team practices that balance AI speed with verification rigour — particularly important for data transformations where subtle errors propagate silently.
• Scale: Design data schemas and storage architectures independently for moderate complexity. Make appropriate trade-off decisions between normalisation and query performance, document design rationale, and consider AI integration points in your designs. Produce consistently high-quality, well-tested data pipeline code and review AI-generated code critically, identifying edge cases and ensuring adequate test coverage.
• Documentation: Create comprehensive documentation for complex data systems. Write precise specifications that enable accurate AI-generated code, establish documentation practices for your projects, and ensure data lineage documentation is discoverable. Identify patterns across data implementations and propose candidates for generalization.
• Reliability: Design observability strategies for your data pipelines. Lead incident response for data quality issues, implement resilience testing for data flows, and conduct blameless post-mortems. Balance reliability investment with delivery velocity. Understand SLIs for data freshness, completeness, and accuracy.
• Process: Optimise team processes systematically, measure and improve cycle time, remove bottlenecks proactively, and deliver rapidly while maintaining quality. Protect focused time for complex work while balancing multiple priorities effectively.
  

Behaviour

• Own the Outcome: Take end-to-end ownership of data pipelines and business outcomes. Accept technical debt intentionally when it accelerates value delivery. Build trust through rapid delivery of working data solutions. Own stakeholder relationships and balance quality with delivery speed. AI may generate the code, but responsibility for data quality and outcomes remains with you.
• Be Polymath Oriented: Bridge gaps between data engineering, software engineering, business, and science. Rapidly immerse in new domains. Speak the language of Commercial operations and make better decisions by understanding the broader business context. See connections across disciplines that others miss — understanding that your data work powers decisions and AI applications downstream.
• Communicate with Precision: Separate requirements, designs, and tasks with precision. Enable AI to generate accurate code through clear specifications. Translate between technical and business language fluently. Facilitate productive discussions and reduce ambiguity in everything you communicate — particularly around data definitions, quality expectations, and pipeline behaviour.
• Don't Lose Your Curiosity: Proactively investigate root causes of data quality issues. Experiment with new technologies and AI capabilities. Protect time for exploration and treat failure as learning. Discover data requirements through immersion in problem spaces rather than waiting for specifications.
• Think in Systems: Map complex data system interactions across technical and business domains. Anticipate cascading effects of changes to data pipelines on downstream consumers. Design data systems that degrade gracefully. Understand how data changes impact business operations and AI applications and factor this into your decisions.
  

Working-level Skills

• Data Integration: You integrate multiple data sources independently, clean, messy datasets, handle inconsistent formats and missing values, and document data lineage. You troubleshoot integration failures.
• Data Modelling: You create efficient data models that balance normalisation with query performance. You optimise queries, handle schema migrations safely, and choose appropriate storage technologies.
• Architecture & Design: You design components and services independently for moderate complexity. You make appropriate trade-off decisions, document design rationale, and consider AI integration points in your designs.
• Problem Discovery: You navigate ambiguous data problem spaces independently. You discover requirements through observation and user shadowing, reframe problems to find higher-value solutions, and distinguish symptoms from root causes.
• Rapid Prototyping & Validation: You deliver working data solutions rapidly (days not weeks). You use prototypes to build stakeholder trust, know when to stop prototyping and start productionising, and balance speed with appropriate quality.
• Business Immersion: You apply deep domain knowledge to data solutions, bridge business and technology conversations fluently, speak the domain language naturally, and shadow operations to build understanding.
  

Foundational-level Skills

• Code Quality & Review: You write readable, well-structured data pipeline code. You use linting tools, write unit tests, and participate constructively in code reviews — both giving and receiving feedback. You review AI-generated code critically.
• Full-Stack Development: You build simple features across frontend and backend. You understand how layers connect through APIs and can debug across the stack. You deliver features end-to-end and make pragmatic technology choices.
• DevOps & CI/CD: You configure basic CI/CD pipelines, understand containerisation, and can troubleshoot common build and deployment failures.
• Cloud Platforms: You deploy applications to cloud platforms and use common data services (compute, storage, databases, queues). You understand cloud pricing and basic security configuration.
• Site Reliability Engineering: You create basic alerts and dashboards. You participate in incident response under guidance.
• AI-Augmented Development: You effectively use AI coding assistants to accelerate work, craft clear prompts, and know when AI helps versus when it slows you down. You don't blindly accept AI suggestions.
• AI Evaluation & Observability: You instrument applications with tracing to capture execution flow. You create evaluation datasets from production data, run basic LLM-as-judge evaluations, and apply pre-built evaluators for common metrics like faithfulness and relevance.
• Multi-Audience Communication: You present complex topics clearly to any audience, facilitate productive discussions, translate between technical and business language fluidly, and write compelling proposals and specifications.
• Stakeholder Management: You proactively update stakeholders on progress, handle basic expectation setting, and escalate concerns appropriately. You build rapport with regular collaborators and manage expectations around delivery timelines.
  

Awareness-level Skills

• AI Literacy: You understand basic AI concepts (training, inference, prompts) and can recognise AI-powered features in products. You know AI has limitations and when traditional approaches may be better.
• Technical Writing: You document your own work following team templates and standards. You keep code comments current and write basic README content.
• Team Collaboration: You collaborate effectively on shared work, support teammates proactively, share knowledge freely, and give and receive feedback constructively. You help onboard new team members.
• Pattern Generalization: You identify patterns across multiple data implementations and propose candidates for generalization. You understand the trade-offs between custom and reusable solutions.
• Data Analysis: You create basic charts and summaries using standard tools. You understand common business metrics and can interpret data quality reports.

What You Bring

• Bachelor's degree in computer science, Data Engineering, Software Engineering, or related field with 5+ years of relevant professional experience.
• Strong production experience with Python, SQL, and cloud data platforms (AWS preferred — including services such as Glue, S3, Redshift, and Lambda; Snowflake, Azure, or GCP also valued) is required.
• Experience with data integration tools, ETL/ELT frameworks, and distributed data processing (such as Spark) is expected.
• Proven ability to design and deliver complete data pipelines end-to-end.
• Demonstrable fluency with AI coding tools (such as Claude Code, Cursor, GitHub Copilot, or similar) and hands-on experience building data infrastructure that supports generative AI applications (data preparation for LLMs, vector databases, RAG systems) is essential.
• Experience with enterprise data platforms (such as Salesforce Data Cloud or similar CRM data systems) is a plus.
Experience navigating ambiguous data landscapes, working directly with business stakeholders, and shipping working solutions rapidly is strongly valued. Experience in an embedded, forward-deployed, or consulting-style engineering model is a strong plus.

Bonus Skills / Experience

• Must have superior communication skills and be able to work under own initiative with a strong sense of ownership.
• This role requires comfort leading in ambiguous environments, building direct trust-based relationships with commercial stakeholders, mentoring junior engineers, and making pragmatic decisions that balance delivery speed with technical quality.
• You will shape team practices and be expected to lead small projects end-to-end.

What We Offer

At Newpage, we’re building a company that works smart and grows with agility, where driven individuals come together to do work that matters. We offer:

• A people-first culture - Supportive peers, open communication and a strong sense of belonging
• Smart, purposeful collaboration - Work with talented colleagues to create technologies that solve meaningful business challenges
• Balance that lasts - We respect your time and support a healthy integration of work and life
• Room to grow - Opportunities for learning, leadership and career development, shaped around you
• Meaningful rewards - Competitive compensation that recognises both contribution and potential

Ready to Apply?

Let’s build the future of health together. Apply below or reach out to:
Ramadevi.bhumireddy@newpage.io

More about Newpage

Newpage is a digital health solutions company. We devote ourselves to advancing the quality of life by enhancing health and optimizing the longevity of people. We do this by, passionately building futuristic technologies for global organizations across the healthcare ecosystem. We partake at every stage from problem definition, strategy & service design, user research, UX design, and agile software development – utilizing lean practices to deliver and validate highly innovative digital health solutions that drive user value and business transformation.

Newpage is recognized by ‘CIO’s Review’ as “Top 50 Promising Healthcare Solution Providers” and Great Place to Work Certified (GPTW) 2023 & 2024.