Top Entry Level Data & Analytics Jobs in Detroit, MI

Designs, implements, and optimizes Salesforce Data Cloud solutions. Responsibilities include configuring data streams, integrating Salesforce and legacy data with MuleSoft or ETL tools, mapping data objects, creating identity resolution rules, building calculated insights with SQL, developing customer segments, configuring activation targets, and managing data governance, privacy compliance, system performance, storage, and usage.

The Technical Business Analyst is responsible for production support of Hadoop platforms, overseeing data acquisition, ingestion, preparation, and access, and managing incident resolution and operational tasks with a team.

The role involves analyzing data in the automotive domain, requiring strong skills in IT security, analysis, organization, and presentation.

Reviews and analyzes large business datasets for accuracy, completeness, relevance, privacy, and quality. Conducts document reviews involving PII, identifies data issues and anomalies, provides feedback to support AI training, refines quality assurance standards, and communicates findings to project stakeholders while maintaining data privacy and security compliance.

Annotate, label, review, and validate audio and video samples according to project guidelines. Identify and correct errors, document annotation decisions, resolve questions with project teams, meet accuracy and deadline requirements, and provide feedback to improve annotation workflows. Strong English communication, attention to detail, organization, and quality control are essential; familiarity with data-labeling tools is advantageous.

Build and maintain production data ingestion pipelines, transformation models, warehouses, and schemas for PerfectServe’s data platform. Deploy reliable, tested pipelines using idempotency and data-quality checks, apply dimensional and medallion modeling, participate in design reviews, and document owned pipelines and runbooks. The role requires SQL, programming experience, dbt or similar transformation tools, cloud data warehouse experience, ELT orchestration, AWS knowledge, and a strong data-testing mindset.

Analyzes institutional data, surveys, student success metrics, enrollment trends, academic information, and KPIs. Prepares internal and external reports, data visualizations, statistical analyses, and presentations supporting university planning, accreditation, strategic initiatives, grants, and program reviews. The role also provides survey design and data-analysis consulting to university departments and ensures data integrity.

Own end-to-end data workflows, including ingestion, storage, orchestration, transformation, validation, and delivery. Build ETL/ELT pipelines, manage cloud data platforms and transfers, optimize data models and queries, maintain data quality monitoring, troubleshoot production issues, and document data flows. Collaborate with technical and nontechnical teams to investigate discrepancies, support enterprise datasets, and apply secure data-handling practices.

Build and maintain finance data pipelines, warehouse models, dashboards, and automations for unit economics, technology budgeting, burn, runway, margins, deal desk, and sales compensation. Develop internal tools and LLM-based agents that help non-engineers access trusted financial data. Partner with finance, sales, and engineering leadership to reconcile discrepancies and improve revenue, cost, billing, payroll, cloud-spend, and accounting workflows.

Supports healthcare data analytics and research projects by evaluating diverse datasets, developing statistical models, validating and cross-referencing data, analyzing care and outcome patterns, and communicating findings to nontechnical stakeholders and clients. The role includes learning healthcare data types, SAS programming, research design methods, and quality improvement analytics while collaborating across multiple teams and managing concurrent projects.

Develops and maintains Power BI dashboards and data models, defines KPIs with stakeholders, validates data accuracy, and collaborates with Data Engineering on optimized analytical models. The role documents data lineage and reporting assumptions, supports self-service BI through communications and training, and automates manual reporting processes. It requires SQL, Power BI, DAX, Power Query, data modeling, and data warehouse knowledge, plus strong communication and cross-functional collaboration skills.