Semantic Layer and AI Integration: Experience designing governed semantic layers, enterprise metrics, business logic, metadata frameworks, and AI-ready data interfaces.
Data Pipeline Engineering: Proven ability to architect and optimize production-grade batch and event-driven pipelines using orchestration tools such as Dagster and cloud-based services.
Data Quality and Engineering Standards: Strong understanding of automated testing, version control, CI/CD, data contracts, observability, lineage, documentation, and governance practices.
Technical Leadership: Experience providing technical direction, mentoring engineers, conducting code reviews, and collaborating with multidisciplinary teams on enterprise technology initiatives.
What the Client Needs You to Do
The organization needs a seasoned analytics engineering professional who can establish the technical foundation for reliable enterprise reporting, advanced analytics, machine learning, and AI-enabled applications. You will lead the architecture and development of reusable data products while defining consistent modeling standards and governed semantic frameworks.
You will also collaborate with data engineering, AI engineering, architecture, research, and business stakeholders to ensure data solutions meet organizational objectives. The successful candidate will bring strong engineering discipline, sound architectural judgment, and the ability to influence technical direction without formal people-management responsibilities.
Key outcomes include
Delivering scalable and trusted analytics data architecture.
Establishing consistent semantic models and enterprise-wide metrics.
Enabling AI applications through structured, contextualized, and governed data.
Improving pipeline reliability, data quality, and engineering efficiency.
Providing technical mentorship and contributing to long-term data and AI strategy.
Key Responsibilities
Architect Enterprise Data Solutions: Lead the design and implementation of scalable data models, analytics-ready data products, and layered lakehouse architectures using modern data technologies.
Establish Modeling Standards: Define reusable modeling patterns, optimize data structures, and resolve complex performance, scalability, and data design challenges.
Develop Semantic Frameworks: Build and govern semantic layers that standardize enterprise metrics, business definitions, relationships, and analytical logic.
Enable AI-Ready Data: Partner with AI engineering teams to develop context engineering strategies, knowledge graphs, metadata frameworks, retrieval approaches, and interfaces supporting technologies such as MCP and text-to-SQL.
Engineer Advanced Data Pipelines: Design and optimize orchestrated batch and event-driven workflows supporting reporting, machine learning, model training, scoring, and embedding generation.
Improve Production Reliability: Implement monitoring, alerting, lifecycle management, and operational practices to maintain dependable data pipeline performance.
Champion Engineering Excellence: Establish best practices for source control, automated testing, CI/CD, technical documentation, and code quality. Conduct architecture assessments and detailed code reviews.
Strengthen Data Governance: Implement data quality frameworks, data contracts, lineage tracking, and observability while ensuring compliance with privacy, security, and responsible AI requirements.
Lead Cross-Functional Initiatives: Coordinate technical planning and solution delivery with data engineering, AI engineering, institutional research, architecture, and business stakeholders.
Mentor and Influence Technical Teams: Provide engineering guidance, facilitate knowledge sharing, evaluate emerging technologies, and contribute to the evolution of analytics engineering 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.
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