Principal Data Engineer

RealTime eClinical Solutions

Principal Data Engineer overseeing AI/ML strategy and driving data solutions for innovative clinical research SaaS company. Enhancing analytical capabilities and technical leadership for data science practices.

Posted 7/23/2026full-timeRemote • Texas • 🇺🇸 United StatesLead💰 $155,000 - $195,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in defining and driving data science and AI roadmaps, with a strong focus on model development, NLP pipeline design, and performance monitoring. Proven ability to translate business problems into actionable data science initiatives while ensuring compliance with regulatory standards.

Highest-signal resume keywords

Data Science Roadmap DevelopmentMachine Learning Solution DeliveryNLP Pipeline DesignStatistical Analysis and Process OptimizationSQL Proficiency for Data Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingMachine LearningNatural Language ProcessingStatistical AnalysisData Pipeline DevelopmentForecasting ModelsData Querying with SQLData ModelingDAX for ReportingProcess Optimization Methodologies

Soft Skills

Clear Communication of Analytical FindingsMentoring and Knowledge TransferCollaboration with StakeholdersAnalytical RigorContinuous Learning

Tools & Technologies

PandasScikit-learnSpaCyHugging Face TransformersPower BI

Industry Keywords

Data GovernanceRegulatory ComplianceHIPAAGDPRLean Six Sigma

Tech Stack

Tools & technologies

PandasPythonScikit-LearnSQL

About the role

Key responsibilities & impact

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.
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.
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).
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.
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.

Requirements

What you’ll 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.

Benefits

Comp & perks

Health insurance
Long-term disability insurance
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
401K matching contributions at 100% of the first 3% and 50% of the next 2%.