Senior Analytics Engineer - Multiple Teams

Job Description

Time Zone Requirement

Resources must be able to provide overlap with the customer's US Eastern Time working hours, including availability through approximately 2-2.30 PM ET (9-10 pm KV time)

About Client

Our client is expanding its data platform team as part of its ongoing data modernization initiatives. They are looking to add two hands-on Data Engineers to support the evolution of the Microsoft-based analytics and data platform.

Role Overview

Senior Analytics Engineer with production experience designing governed analytical and semantic architectures on Microsoft Fabric and Power BI, with particular depth in semantic models, enterprise metrics, Direct Lake, OneLake, and AI-enabled analytics.

Designs the semantic layer that connects governed data products to Power BI, self-service analytics, and AI/agentic consumption. Defines how business concepts, relationships, measures, metrics, metadata, and ontologies are represented so that humans, reports, and AI agents interpret enterprise data consistently.

Strong in Power BI semantic modeling, DAX, TMDL/PBIP, Direct Lake architecture, OneLake Security, reusable metric design, model performance, and enterprise semantic governance. Understands how Fabric Data Agents and other AI consumers depend on well-designed semantic models.

Works across data engineering, analytics engineering, governance, platform, AI, and business-domain teams to establish reusable architecture patterns rather than building isolated models or reports.

Must Have

Core Technical Strengths

• Semantic model architecture: Designs production-grade Power BI and Fabric semantic models with well-defined fact/dimension grain, star schemas, relationships, reusable measures, calculation logic, hierarchies, business-friendly metadata, and clearly bounded analytical domains. Determines what belongs in the Lakehouse, semantic model, metric layer, and presentation layer rather than allowing business logic to proliferate across reports.
• Direct Lake & Microsoft Fabric: Deep understanding of Direct Lake semantic models over governed OneLake data products, including model design, Delta-table dependencies, framing/source-state considerations, performance, security, deployment, and production validation.
• Enterprise metrics & semantic governance: Establishes centralized, reusable business metrics with clear definitions, ownership, grain, authoritative implementations, dependencies, and lifecycle management. Designs semantic measures so reports and downstream consumers reuse governed definitions rather than recreating DAX or business rules independently. Understands when a metric belongs in the Lakehouse, semantic model, or requires coordinated SQL and DAX implementations.
• Data Agents & AI-ready analytics: Designs semantic models and governed data products for consumption by Fabric Data Agents and broader agentic solutions. Understands that successful AI analytics depends not only on model access, but on unambiguous business terminology, model descriptions, relationships, measures, etc.
• OneLake Security & identity-aware analytics: Designs analytics solutions that preserve appropriate authorization from governed OneLake data products through semantic consumption. Understands OneLake data access roles alongside Power BI model security such as RLS and the implications of Import versus Direct Lake patterns.
• Power BI engineering & performance: Advanced DAX and data-modeling skills with the ability to diagnose model and query performance using Power BI Performance Analyzer, DAX Query View, DAX Studio, model metadata, relationship analysis, and representative workload testing.
• Reusable analytics architecture: Creates reference architectures, development standards, templates, validation patterns, and reusable components that multiple domains can adopt. Balances central governance with domain ownership so teams can independently build data products and analytical experiences
• Understanding of Fabric REST APIs and metadata for designing Audit & Monitoring solutions for Observability.
• Understanding of data modeling and data engineering concepts with Medallion architecture and data mesh concepts
• Python experience preferred but not required

Working Style

• Starts with the semantic contract, not the visualization**.** Establishes business concepts, grain, entities, relationships, measures, ownership, security, and consumption requirements before designing reports.
• Works comfortably across organizational boundaries**.** Able to communicate effectively to business users and DPMs to articulate considerations, approaches, limitations and trade-offs.
• Can work independently and manage and prioritize sprint objectives and workloads

What You Will Love About Working Here

• We care about all our employees and want them to feel as comfortable as possible. That's why we offer them health insurance from the first days, regardless of the probationary period.

• The gift from the company - Christmas holidays from 25 December to 31 December.

• Сooperation with Superhumans center and Veteran HUB. Capgemini Engineering has supported the launch of psychological rehabilitation department of Superhumans. Our team also donated over UAH 500 000 prosthetics for three Ukrainian defenders. Currently, we support psychological counseling provided by the Veteran Hub, and we have implemented an internal policy making the company friendly to military and veterans with the assistance of the Hub.