Senior Data Power BI Engineer

Job Title

Senior Data Power BI Engineer

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 & Modeling

 Build optimized Power BI data models (star/snowflake, ETL within Power Query,

composite models, DirectQuery/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 & Integration

 Work with SQL, 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, BigQuery).

 Implement incremental refresh, partitioning, and performance optimization

strategies for large datasets.

Dashboarding & 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 Query Plans, Visual Interactions, and Synapse/Snowflake query

profiles to resolve bottlenecks.

Governance, Quality & 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 & 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 SQL across major relational databases (SQL Server, Oracle, Snowflake,

Synapse, BigQuery, etc.).

 Strong experience with cloud data platforms (Azure preferred: Synapse,

Databricks, Azure SQL, 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.

 Quality & Governance: Ensures accuracy, consistency, and standardization across

metrics and datasets.

 Scalability Mindset: Designs solutions that support enterprise growth and

self-service analytics.

 Collaboration: Works eƯectively with engineering, architecture, and business

teams.