Data Engineer

Apex Systems, Inc; Dearborn (MI); On-site; USD 120,000 - 170,000; Full time

Job summary

Apex Systems, Inc. is seeking a highly experienced Data Engineer to drive Power BI semantic model design and BigQuery data engineering. You will lead analytics initiatives, design scalable BI solutions, and collaborate with stakeholders to translate reporting needs into high-impact insights.

You will architect enterprise-grade Power BI models, optimize BigQuery pipelines, and establish standards for data governance and performance across the organization.

Qualifications

• 10+ years of data engineering and BI experience.
• Experience architecting enterprise Power BI semantic models.
• Strong BigQuery and SQL optimization skills.

Responsibilities

• Lead data visualization initiatives with BI stakeholders.
• Design and maintain Power BI semantic models.
• Develop and optimize BigQuery ETL/ELT pipelines.
• Collaborate with cross-functional teams to translate reporting needs.
• Establish data quality and performance standards.

Skills

Power BI

Semantic model

DAX measures

M-Query

BigQuery SQL

ETL pipelines

Agile

Data governance

Tools

Power BI Desktop

BigQuery

SQL

Job description

Job Description

About the Role We are seeking a highly experienced Data Engineer with deep expertise in Power BI semantic model design and GCP BigQuery to lead data visualization initiatives within our growing analytics team. The ideal candidate brings 10+ years of experience in data engineering and business intelligence, along with a proven track record of architecting, developing, and optimizing enterprise-scale Power BI semantic models, reports, and dashboards. Strong data engineering knowledge is also essential. This role will establish best practices for querying and transforming massive datasets in BigQuery and partner with business and cross‑functional stakeholders to translate reporting needs into high-performance, high‑impact insights.

Key Responsibilities

A. Business Engagement and Requirement Discovery

• Meet regularly with business stakeholders, process owners, and end users to understand reporting needs, pain points, and the decisions reports must support.
• Lead requirements workshops and walkthroughs of existing reports to identify gaps, manual work, and duplicate reports that can be consolidated.
• Define and document KPIs, metrics, and business rules in a shared glossary, including calculation methods, filters, and sources of truth.
• Document reporting logic in detail, including:
  • Report grain, such as dealer, contract type, or quarter
  • Filters, exclusions, and default values
  • Hierarchies, drill paths, and time‑intelligence requirements
  • Treatment of nulls, negative values, and cancelled or reversed records
  • Confirm data sources, refresh frequency, and latency expectations, including daily, hourly, and near‑real‑time requirements.
• Gather security requirements, including data visibility and row‑level or object‑level security.
• Establish acceptance criteria, sample outputs, and sign‑off requirements before development begins.
• Translate business requirements into functional and technical specifications, mapping documents, and logical data models.
• Prioritize the backlog with business stakeholders and establish delivery milestones using agile practices.
• Lead UAT and reconciliation sessions by comparing report totals with source systems or legacy reports.
• Provide user training, documentation, and ongoing support.

B. Semantic Model and Data Modelling

• Design and maintain enterprise Power BI semantic models using star and snowflake schemas.
• Select the appropriate storage mode—Import, DirectQuery, Dual, Direct Lake, or composite models—based on data volume, freshness, and cost.
• Develop robust models with:
  • Optimized relationships
  • Reusable, well‑documented DAX measures and calculation groups
  • M‑Query transformations
  • Role‑playing dimensions
  • Incremental refresh and aggregation tables
  • Promote reusability through shared certified datasets, thin reports, and consistent business definitions across departments.
• Establish standards for naming conventions, folder and measure organization, and model documentation.

C. GCP BigQuery and Data Engineering

• Write complex, optimized BigQuery SQL using CTEs, window functions, and joins across large datasets.
• Apply BigQuery best practices, including partitioning, clustering, views, materialized views, query pruning, avoiding SELECT *, slot and on‑demand usage management, and query performance tuning.
• Collaborate with data engineers to design ETL/ELT pipelines, curated (gold) layers, and dimensional models in BigQuery.
• Build reporting‑ready views or tables in BigQuery so that intensive transformations occur upstream rather than in Power BI.
• Ensure data accuracy, integrity, and consistency through validation checks, reconciliation,