Principal Data Engineer

“Be part of a company that is influential and the standard for a rapidly evolving industry!” WHO ARE WE? RealTime eClinical Solutions is a Global Leader and rapidly growing SaaS technology company that provides comprehensive Software Solutions to the clinical research industry. Our Vision is to reshape the global clinical research industry with innovative solutions that help advance medicine and save lives. Our cloud-based solutions are dedicated to solving complex problems and simplifying clinical research processes to be more organized, efficient, and cost-effective. We are based out of San Antonio, TX but are truly a remote and telecommuting company. WHAT ARE WE LOOKING FOR? The Principal Data Engineer serves as the technical and analytical authority for the organization’s data science practice. This is a senior individual contributor and team lead role responsible for driving AI/ML strategy, delivering data-driven solutions, and elevating the analytical capability of the wider team. The role combines deep technical expertise in machine learning, NLP, and forecasting with strong product ownership and stakeholder communication skills, ensuring that data science investments translate into measurable business outcomes. WHAT WILL YOU BE DOING? 1. DATA SCIENCE & AI LEADERSHIP * Define and drive the data science and AI roadmap, aligning model development priorities with business objectives and product strategy. * Lead end-to-end delivery of ML and AI solutions — from problem framing, data discovery, and model design through validation, deployment, and performance monitoring. * Translate ambiguous business problems into well-scoped data science workstreams, identifying quick wins alongside longer-term strategic initiatives. * Champion best practices in model development, including versioning, documentation, validation, and observability. 2. NLP, FORECASTING & ADVANCED ANALYTICS * Design and implement NLP pipelines for use cases such as entity extraction, semantic mapping, classification, and retrieval-augmented generation (RAG). * Build and maintain forecasting and predictive models to support operational and strategic decision-making. * Apply statistical and machine learning methods to identify root causes of process inefficiencies and data quality issues. * Develop reusable data pipelines, crosswalk tables, and transformation workflows that support scalable, cross-functional data products. 3. DATA & PROCESS ANALYSIS * Conduct current-state assessments of data architecture, sources, and quality; define future-state data models and governance standards. * Develop and maintain KPI reporting frameworks and dashboards that enable performance monitoring and data-driven decision-making. * Apply process optimization methodologies (e.g., Lean Six Sigma) to identify bottlenecks, reduce cycle time, and improve data accuracy. * Ensure analytical outputs are accurate, auditable, and aligned with regulatory and compliance requirements (e.g., HIPAA, GDPR). 4. STAKEHOLDER COLLABORATION & COMMUNICATION * Partner closely with Product, Engineering, and business stakeholders to clarify requirements, validate feasibility, and define measurable success criteria. * Communicate complex analytical findings and model outputs clearly to both technical and non-technical audiences, including executive stakeholders. * Define value-realization strategies for data and AI investments, ensuring ROI is tracked through improved search, reporting, and operational insight. 5. TEAM DEVELOPMENT & KNOWLEDGE TRANSFER * Mentor data analysts and junior data scientists through pairing, design reviews, and structured technical guidance. * Lead knowledge transfer of owned models, pipelines, and analytical frameworks to ensure team resilience and continuity. * Drive a culture of continuous learning, analytical rigor, and responsible AI within the data science function. Performance at this level is evaluated across four dimensions: Technical Quality * Models, pipelines, and analytical frameworks consistently meet peer review standards and produce reliable, reproducible results. * Data quality, model drift, and technical debt in areas of ownership trend downward over time. DELIVERY & IMPACT * Data science deliverables are completed on schedule with accurate effort estimation; risks and blockers are surfaced early. * Analytical insights are actionable, with measurable improvements in KPIs such as process efficiency, data accuracy, or cost reduction. ORGANIZATIONAL IMPACT * Data analysts and engineers who regularly collaborate with this role demonstrably improve analytical judgment and technical practice. * Data science recommendations are trusted by peers, product leadership, and executive stakeholders without requiring repeated validation. COMMUNICATION & LEADERSHIP * Ambiguous analytical problems are framed and decomposed independently, without waiting for direction. * Findings and model outputs are presented in a way that drives clear decisions by non-technical stakeholders.  WHAT DO YOU NEED? * Bachelor’s degree in data science, Computer Science, Mathematics, Statistics, Economics, or a related quantitative field, or equivalent professional experience. * 5+ years of experience in data science, data analytics, or a related discipline, including production ML/AI deployments. * Strong proficiency in Python for data science workflows, including pandas, scikit-learn, and NLP libraries (e.g., spaCy, Hugging Face Transformers). * Proven experience designing and delivering NLP pipelines and/or forecasting models in a business context. * Solid command of SQL for data querying, transformation, and analysis across relational databases. * Experience with BI and reporting tools, particularly Power BI, including data modeling and DAX. * Demonstrated ability to communicate analytical findings clearly to non-technical stakeholders and drive decision-making. * Experience working in regulated industries (healthcare, finance, or similar) with an understanding of compliance and data governance requirements.  WHAT SETS YOU APART? * 7+ years of data science or analytics experience, including a team lead or principal contributor role. * Experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and prompt engineering for enterprise use cases. * Familiarity with cloud-based ML platforms (GCP, AWS, or Azure) and MLOps practices. * Experience with process automation tools (e.g., UiPath or similar RPA platforms). * Working knowledge of process optimization frameworks such as Lean Six Sigma (Green Belt or higher). * Exposure to clinical data standards, health data interoperability, or cross-client data standardization projects. * Proficiency in data visualization and dashboard design; PL-300 Power BI Data Analyst certification is a plus. WHAT IS IN IT FOR YOU? * The company sponsors health insurance, long-term disability, and life insurance. * Unlimited Paid Time Off. * 10 paid Holidays. * Paid Parental Leave. * Work Anniversary Bonus. * Participation in the Employee of the Quarter Program. * Monthly $100 Connectivity Stipend Reimbursement. * RealTime matches employee 401K contributions at 100% of the first 3% invested and 50% of the next 2% invested. All successful candidates must complete and pass reference and background checks. The desired salary must be indicated for the application to be considered. The pay rate is commensurate with experience and is determined on an individual basis after an interview has occurred. Equal Opportunity Employer – RealTime eClinical Solutions strongly values diversity and is committed to equal opportunity and non-discrimination in all of its policies and practices, including employment. Your Right to Work – In compliance with federal law, all persons hired will be required to verify identity and eligibility.        Thank you for your interest in RealTime eClinical Solutions.