Senior Data Power BI Engineer

Senior Data Power BI Engineer

Contract: 6 Months

Remote

Role Summary

The Senior Data Power BI Engineer designs, develops, and optimizes enterprise-grade BI solutions that turn complex data into clear, actionable insights. This role builds

high-performance Power BI semantic models, advanced DAX calculations, scalable datasets, and visually compelling dashboards while partnering closely with data engineering, architecture, and business stakeholders to deliver governed, reliable analytics at scale.

Key Responsibilities

Power BI Development G Modeling

Build optimized Power BI data models (star/snowflake, ETL within Power uery, composite models, Directuery/Import strategies).

Develop complex DAX measures, calculation groups, and advanced time-intelligence logic.

Design and publish enterprise-certified datasets, semantic layers, and standardized metrics for cross-team use.

Data Engineering G Integration

Work with SL, views, stored procedures, and cloud data platforms to prepare clean, performant data sources.

Collaborate with data engineers on ETL/ELT pipelines (dbt, ADF, Databricks, Snowflake, Synapse, Biguery).

Implement incremental refresh, partitioning, and performance optimization strategies for large datasets.

Dashboarding G Visualization

Build enterprise dashboards that follow UI/UX best practices, actionable storytelling, and usability standards.

Deliver executive views, operational reporting, and advanced self-service analytics capabilities.

Implement row-level security (RLS), object-level security (OLS), and appropriate governance controls.

Performance Optimization

Troubleshoot dataset refresh failures, optimize DAX, and improve query performance through modeling best practices.

Analyze DAX uery Plans, Visual Interactions, and Synapse/Snowflake query profiles to resolve bottlenecks.

Governance, uality G Standards

Establish and enforce Power BI development standards, naming conventions, and workspace governance.

Support data cataloging, lineage documentation, and metric definitions with Data Governance teams.

Conduct code reviews, dataset validation, and promote reuse of models and components.

Collaboration G Stakeholder Enablement

Translate business requirements into scalable BI solutions with clear KPIs, metrics, and analytical logic.

Work with cross-functional teams business SMEs, analysts, architects, engineers to deliver consistent insights.

Mentor junior developers and support enterprise adoption of BI best practices.

Required Qualifications Experience

5 8+ years in BI development, data modeling, or analytics engineering with deep Power BI expertise.

Technical Skills

Expert-level Power BI: DAX, Power Query (M), tabular modeling, calculation groups, semantic modeling.

Advanced SL across major relational databases (SL Server, Oracle, Snowflake, Synapse, Biguery, etc.).

Strong experience with cloud data platforms (Azure preferred: Synapse, Databricks, Azure SL, Lakehouse).

Familiarity with ETL/ELT tools (ADF, Databricks, dbt, SSIS, or similar).

Knowledge of data warehousing concepts (dimensional modeling, fact tables, SCD patterns).

Experience with Git and deployment pipelines (Power BI deployment pipelines, Azure DevOps, GitHub).

Soft Skills

Strong communication and ability to translate technical concepts for non-technical audiences.

Analytical mindset, attention to detail, and problem-solving skills.

Ability to manage multiple projects and work independently in a fast-paced environment.

Preferred Qualifications

Experience with Power BI Admin, workspace governance, capacity management, and tenant settings.

Knowledge of data governance tools (Purview, Collibra, Alation).

Exposure to Python for data wrangling or automation.

Experience building BI solutions for executive audiences or large enterprise environments (>1,000 users).

Familiarity with semantic layer tools (Metric Stores, Fabric Semantic Models, LookML equivalents).

Core Competencies

Technical Mastery: Deep understanding of BI engineering, modeling, and performance tuning.

Data Storytelling: Ability to translate complex datasets into clear visual narratives.

uality G Governance: Ensures accuracy, consistency, and standardization across metrics and datasets.

Scalability Mindset: Designs solutions that support enterprise growth and self-service analytics.

Collaboration: Works effectively with engineering, architecture, and business teams.