`Why Work Here
- Headquarters is in Charlotte, NC
- Recently expanded to nearshore
- isolved ranked for SMB Payroll in 2023 Sapient Report
- Voted top places to work in USA 2023
We're looking for a Principal Data Engineer to lead the development of our modern, intelligent data platform. This role blends deep expertise in data engineering, machine learning infrastructure, and data product development. You will architect and build scalable, cloud-native solutions that enable everything from classic analytics to Retrieval-Augmented Generation (RAG) and advanced feature engineering. You should be equally comfortable modeling a data warehouse in dbt as you are optimizing storage for vector embeddings.
Key Responsibilities:
- Design and implement scalable data models using dimensional modeling and best practices for modern data warehousing.
- Develop and scale dbt transformation pipelines, including testing, documentation, and modularization.
- Architect and optimize Delta Lake / Apache Iceberg tables for high-performance analytics and large-scale ingestion.
- Build production-grade processes in Python, integrating with various data sources and APIs.
- Implement and manage KNN embedding storage (e.g., FAISS, Pinecone, Weaviate, or Azure AI Search) for use in RAG pipelines and ML-powered search or recommendations.
- Establish a foundation to support feature engineering, dataset versioning, and model-ready pipelines.
- Enable Retrieval-Augmented Generation (RAG) use cases by building reliable retrieval layers and embedding pipelines to support LLM-based systems.
- Define architecture, development and lifecycle of data products that serve internal users, operational processes, and customer-facing applications.
- Champion cloud-native development practices, primarily on Azure (preferred) including storage, compute, and orchestration tools.
- Establish best practices in CI/CD, observability, testing, governance, and documentation across the data stack.
- Provide technical mentorship and thought leadership across engineering and analytics teams.
Required Skills & Experience:
- 8+ years in data engineering or similar technical roles.
- Deep hands-on experience with dbt, SQL, and data warehouse modeling (Kimball, Data Vault, etc.).
- Experience with Apache Iceberg and/or Delta Lake in production-grade systems.
- Expertise in Python and Spark for scalable data processing.
- Experience building and operating KNN/vector databases and embedding stores (e.g., FAISS, Pinecone, Azure AI Search, Weaviate, etc.).
- Familiarity with Retrieval-Augmented Generation (RAG) architecture and implementation best practices.
- Solid understanding of feature engineering workflows and their role in ML and data products.
- Proven ability to build and scale data products that drive insights, decisions, and customer experiences.
- Cloud platform experience, with a strong preference for Azure
- Experience with IaC [Terraform, etc]
- Understanding of data governance, privacy, and compliance frameworks (e.g., GDPR, HIPAA, SOC 2).
Nice to Have:
- Familiarity with ML model pipelines (e.g., feature stores, model registries).
- Experience with orchestration tools like Airflow, dbt Cloud, or Azure Data Factory.
- Understanding of modern LLM infrastructure and vector-enhanced applications.
- Desire or experience in Agentic AI
- Experience working in agile product teams or a startup environment.
In adherence to relevant pay transparency legislation and regulations, we endeavor to offer clarity regarding our compensation methodology for this position. While specific pay ranges are presently under review to ensure alignment with competitive market standards, we will furnish this information upon finalization.
At our organization, individual compensation structures are established through a consistent and equitable process, taking into account various pertinent factors. These factors encompass geographic location, job-specific competencies, educational attainment, professional certifications, and pertinent experience. This structured approach enables us to align our total rewards package competitively with each employee's qualifications, duties, and contributions to our collective success.
An employee's comprehensive compensation package comprises diverse components, such as base salary, performance-linked bonuses or commissions, long-term incentives like equity grants, and an extensive benefits portfolio. Should you advance as a finalist candidate, you will have the opportunity to explore our overarching compensation philosophy, practices, and how your unique background informs an appropriate compensation proposition.
Our commitment lies in fostering a fair, impartial, and transparent compensation framework throughout our workforce. All compensation determinations are aimed at justly acknowledging and rewarding the value, qualifications, and significant contributions that our employees bring to their roles and to the company's achievements. We welcome your inquiries about our principles of pay equity, accompanying analyses, and associated procedures throughout the recruitment process.
