Modern Data Engineering Tools: Hands-on experience with dbt or comparable transformation frameworks, along with familiarity with cloud-based data platforms.
Data Modeling and Architecture: Strong understanding of dimensional modeling, layered data architectures, semantic models, and enterprise data design principles.
Software Engineering Practices: Experience with Git, version control, code reviews, automated testing, continuous integration, continuous deployment, and workflow orchestration.
Cloud and AI Technologies: Experience with cloud lakehouse environments, preferably AWS S3, Apache Iceberg, and Trino. Exposure to large language models (LLMs), retrieval-augmented generation (RAG), embeddings, or AI-powered analytics is desirable.
Communication and Governance: Ability to independently manage complex assignments, communicate technical concepts effectively, and follow established data privacy, security, and governance standards.
What the Client Needs You to Do
The organization needs an experienced analytics engineering professional who can transform raw enterprise data into reliable, well-structured, and reusable data products. You will develop analytics-ready datasets, implement governed semantic models, and build data pipelines that support business intelligence, machine learning, and AI-driven applications.
A key priority is creating the business context that allows AI systems and large language models to interpret organizational data accurately. You will work alongside data engineers, AI engineers, analysts, faculty, and business stakeholders to translate functional requirements into scalable technical solutions.
Key outcomes include
Delivering accurate, reusable, and well-documented analytics data models.
Establishing consistent business definitions and metrics through semantic modeling.
Developing AI-ready metadata and contextual information for LLMs and AI agents.
Building reliable data pipelines supporting analytics and machine learning workflows.
Improving data quality, observability, security, and engineering standards.
Supporting continuous improvement through emerging data and AI technologies.
Key Responsibilities
Develop Analytics Data Models: Design, build, test, and maintain modular data models and transformations using dbt, SQL, and Python to support reporting, analytics, and AI applications.
Implement Data Architecture Patterns: Apply dimensional modeling, layered architecture, and medallion design principles while contributing to technical architecture and solution development.
Optimize Data Performance: Improve query efficiency, model usability, and processing costs while troubleshooting and enhancing existing data assets.
Build Governed Semantic Layers: Implement enterprise metrics, business logic, and entity relationships to maintain consistent data definitions across reporting platforms and AI applications.
Develop AI Context Frameworks: Create metadata, schema descriptions, technical documentation, lineage information, and structured business context that enable LLMs and AI agents to understand and query data accurately.
Validate AI-Driven Analytics: Test natural language querying, text-to-SQL capabilities, and AI-generated analytical outputs. Refine semantic models and contextual information to improve accuracy and reliability.
Engineer Data Pipelines: Build, maintain, and monitor orchestrated workflows using Dagster and AWS services to support analytics, machine learning, and AI-enabled applications.
Prepare AI and ML Datasets: Develop curated datasets, machine learning features, and embeddings supporting model development, RAG workflows, and AI analytics in collaboration with engineering teams.
Maintain Data Quality and Governance: Implement automated data tests, data contracts, and observability practices while following established security, privacy, and compliance requirements.
Apply Engineering Best Practices: Use version control, peer reviews, automated testing, CI/CD, and technical documentation standards to ensure maintainable and dependable data solutions.
Collaborate with Stakeholders: Partner with technical teams, analysts, faculty, and business representatives to translate research and operational requirements into data models, metric definitions, and technical specifications.
Stay Current with Emerging Technologies: Evaluate modern analytics engineering tools, AI technologies, and development practices to continuously improve data solutions and technical capabilities.
W2 employees of Overture Partners who work 30 or more hours per week are eligible for the following benefits: medical (choice of 3 plans), 401(k) starting on day one, a variety of voluntary benefits including life and disability insurance, and sick time if required by law in the worked-in state/locality.
#25697
