Principal Data Engineer

REALTIME SOFTWARE SOLUTIONS LLC• Remote, US,

20h•$155,000 - $195,000•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.