Enterprise Data Analytics and AI Developer

Dudek

1d•$140,000 - $170,000•Remote

About The Position

The Enterprise Data Analytics and AI Developer is responsible for the design, implementation, and deployment of enterprise-scale data management, data analytics, and AI capabilities. The role leverages Microsoft Fabric (including OneLake, Data Factory, lakehouses, warehouses, and Power BI) and Microsoft AI Foundry (including model catalog, agent service, evaluations/observability, and control planes) to deliver secure, governed, and reliable solutions. Working across traditional corporate organizations and lines of business, this role ensures solutions are delivered to specification, aligned with enterprise architecture standards, resilient in production, and optimized for cost, performance, and compliance.

Requirements

7+ years of hands-on experience in data engineering, analytics engineering, and/or AI application development in enterprise environments.
Deep understanding of data modeling frameworks: Kimbal, EDW Streaming and Lakehouse Architecture.
Expertise with Microsoft Fabric: OneLake, Data Factory, Lakehouse, Warehouse, Real-Time Intelligence/KQL, and Power BI semantic models.
Expertise with Microsoft AI Foundry: model catalog, Agent Service, evaluations/observability, safety/guardrails, and Control Plane.
Proficiency in SQL, Python, and KQL; experience with data modeling (star, data vault), and performance tuning.
Experience implementing RAG pipelines (Azure AI Search/vector indices) and securely grounding agents to governed enterprise data.
Proven delivery of production-grade solutions with CI/CD, IaC, automated testing, and operations runbooks on Azure.
Strong understanding of data governance, privacy, and security (RBAC, sensitivity labels, row-level/object-level security, DLP).
Excellent communication skills with the ability to explain complex technical topics to diverse stakeholders.
Must possess a valid driver’s license and have active personal automobile liability insurance by the first day of employment
Deep understanding of Azure tooling.
DevOps, Quality, and Operations

Nice To Haves

Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or related field.
Certifications such as Microsoft Certified: Azure Data Engineer Associate, Azure AI Engineer Associate, or Microsoft Fabric certifications.
Experience with Purview governance, DLP policies, and compliance frameworks in regulated industries.
Experience integrating ERP’s or other enterprise business systems.
Ability to interoperate within multi-platform services.

Responsibilities

Partner with enterprise architecture & engineering to define data and AI roadmaps that align with business objectives and operating models.
Develop reference architectures and patterns for Fabric (OneLake, Lakehouse/Warehouse, Data Factory) and AI Foundry (agents, grounding, evaluations, guardrails).
Shape data service standards (naming, domains, data contracts), semantic modeling conventions, and model lifecycle policies.
Contribute to backlog planning, estimation, release planning, and solution sizing for enterprise programs.
Influence security, privacy, and compliance requirements (RBAC, sensitivity labels, DLP) for data and AI workloads.
Provide technical leadership and mentorship to technical teams and practitioners while establishing code review, testing, and deployment standards.
Translate business outcomes into technical designs and acceptance criteria; communicate tradeoffs and risks to non-technical stakeholders.
Collaborate with corporate governance teams to ensure responsible AI and governed data usage.
Enable knowledge transfer with high-quality documentation, runbooks, and enablement sessions for end users and support teams.
Lead end-to-end technical delivery for multiple initiatives—from discovery and design through build, test, release, and operations.
Define technical work breakdown structures (WBS), estimates, and resource plans; provide progress updates tied to backlog items and milestones.
Own technical quality gates: design reviews, data model reviews, security reviews, and production readiness assessments.
Coordinate integration with third-party systems and data providers; support vendor RFP/SOW technical inputs and evaluation criteria.
Drive non-functional requirements (performance, availability, observability, cost) and execute performance/scalability tests prior to go-live.
Facilitate UAT, cutover planning, and incident response playbooks; ensure smooth transitions to operations.
Design, configure, and deploy custom copilots using Microsoft’s Copilot studio
Train technical users on the design and prototyping of custom copilots
Integrate custom copilots into Teams and Sharepoint user interfaces
Design Lakehouse and Warehouse architectures in OneLake and implement domain-driven data services.
Build ingestion and transformation ETL pipelines with Data Factory, notebooks, shortcuts, and mirroring
Develop Power BI semantic models and datasets; optimize aggregations, partitions, incremental refreshes, and query performance.
Implement KQL databases for streaming/operational analytics and monitoring use cases.
Harden solutions with OAuth, SAML assertions, RBAC, sensitivity labels, row-level/object-level security, and workspace isolation; integrate with Purview where applicable.
Automate CI/CD for Fabric items (Lakehouse, Warehouse, Semantic Models, Data Factory) using deployment pipelines.
Select and evaluate models via the model catalog and implement model router policies and versioning/upgrade strategies.
Build and host single and multi-agent solutions with Agent Service while integrating frameworks agent frameworks as needed.
Implement retrieval-augmented generation (RAG) using Azure AI Search & vector indices while securely grounding agents with Fabric Data Agents where applicable.
Instrument tracing, evaluations, and guardrails and configure data leakage prevention per enterprise policy.
Operate with the Foundry control plane for fleet governance, cost controls, and policy enforcement while integrating alerts with enterprise monitoring.
Implement Machine Learning tools to design and train AI models to solve business challenges.
Experience with data pipelines related to enterprise structured and unstructured data models.
Establish CI/CD for data and AI assets using GitHub and implement environment promotion, approvals, and rollback strategies.
Create automated tests (unit, pipeline, data quality, prompt/agent evals) and define associated runbooks
Set up cost observability and right-size capacity and throughput.
Implement telemetry and logging for data pipelines, query performance, agent runs, tool calls, error handling, and publish operational dashboards.