Data Analyst

Dolese Bros•Oklahoma City, OK

1d•Hybrid

About The Position

As a Data Analyst at Dolese, you will play a key role in transforming data into actionable insights that support business and operational decision-making across the organization. This role is responsible for applying advanced analytics techniques, data modeling, dashboard/report development, and collaborating with business partners to identify opportunities for performance improvement. The Data Analyst supports the organization’s analytics capabilities by applying established machine learning and statistical techniques within business intelligence workflows, contributing to data quality and reporting improvements, and supporting analytics initiatives.

Requirements

Bachelor’s degree in Data Analytics, Statistics, Computer Science, Business, Engineering, or related field required
Minimum of four (4) years of hands-on analytics experience working with large or complex datasets.
Strong working knowledge of SQL and data querying, data visualization tools (Power BI strongly preferred), analytical/BI tools, data modeling concepts, and foundational ML/AI concepts.
Strong understanding of data warehousing, ETL/ELT concepts, relational and columnar database structures, and established modern data architecture patterns (e.g., data lakehouse, medallion architecture) used to support analytical workloads.
Ability to translate business needs into effective analytical solutions that drive insight and decision-making.
Strong analytical mindset with strong critical thinking skills.
Excellent communication and data storytelling abilities.
Proven ability to build effective stakeholder partnerships that align analytics to business needs.
Demonstrated technical curiosity and continuous-learning mindset, with active interest in emerging AI/ML and BI technologies.
Proven attention to detail and commitment to high standards for accuracy.
Ability to manage multiple priorities in a fast-paced environment while maintaining focus and quality.

Nice To Haves

Master’s degree in Data Science, Analytics, Statistics, or related field preferred.
Experience with cloud analytics platforms such as Azure Synapse, Databricks, or Snowflake preferred.
Proficiency in Python or R for advanced analytics, applied machine learning use cases, or automation preferred.
Familiarity with libraries such as scikit-learn, pandas, or statsmodels preferred.
Hands-on experience applying predictive modeling and machine learning techniques (e.g., regression, classification, clustering, time-series forecasting) to business intelligence use cases preferred.
Familiarity with MLOps concepts, model monitoring, and AI governance practices as related to supporting reliable deployment of ML models in production BI environments preferred.
Industry experience in energy, manufacturing, healthcare, or customizable services preferred.

Responsibilities

Analyze complex datasets to uncover trends, patterns, and opportunities that drive strategic and operational decisions.
Apply predictive models, approved machine learning techniques, and statistical analyses to forecast outcomes, support scenario planning, and enhance business intelligence.
Translate analytical findings into clear, compelling insights and recommendations for business stakeholders.
Leverage AI-assisted tools (e.g., Copilot, generative AI, natural language query) to accelerate insight discovery and enhance self-service BI capabilities for business users.
Develop, refine, and maintain semantic models, datasets, and curated data structures used for reporting, analysis, and supporting machine learning use cases.
Support enterprise data governance by ensuring accuracy, consistency, and quality across analytics assets.
Collaborate with data engineering teams to improve data pipelines, definitions, and reliability, and support scalable data architecture that enables BI and analytics workloads.
Build and maintain enterprise dashboards and reports using tools such as Power BI, Tableau, or similar platforms.
Ensure visualizations follow best practices for clarity, usability, and performance.
Continuously enhance existing reports based on stakeholder feedback and evolving business needs.
Integrate AI-powered features into BI platforms, such as anomaly detection, intelligent alerting, and ML-driven forecasting embedded in dashboards.
Work closely with business leaders to understand their objectives and translate them into analytic requirements.
Partner with business stakeholders to provide guidance on KPIs, metrics, and measurement strategies that support informed decision-making.
Clearly communicate complex analytical results to non-technical audiences.
Provide informal guidance and knowledge sharing to other analysts on analytical methods, AI/ML techniques, BI tools, and best practices.
Support continuous improvement of analytics processes, including automation, documentation, and standardization.
Contribute feedback to established analytics standards, templates, and frameworks.