Applied Analytics Engineer

Vulcan Materials Company•Irving, TX

About The Position

We are seeking a highly analytical professional with 3–5 years of demonstrable experience to join our team as an Applied Analytics Engineer. The successful candidate will be responsible for the full-spectrum of technical work, with a primary emphasis on building, optimizing, and maintaining robust data pipelines that ingest, clean, and transform large, complex datasets for analytical readiness, and developing, rigorously testing, and deploying high-impact Machine Learning models into production systems for live use. The ideal candidate will translate complex business problems into measurable, scalable data solutions that drive strategic decision-making and business outcomes.

Requirements

5 years of experience with statistical and programming languages for data analysis, specifically Python (including PySpark, NumPy, Pandas, Scikit-learn) and SQL.
5 years of demonstrable experience in a data-focused role encompassing data exploration, data cleaning, and data visualization. Experience with cloud platforms (AWS, Azure, GCP)
Practical experience with big data processing frameworks such as Spark or similar distributed computing environments.
Extensive experience developing predictive data models, quantitative analyses, and visualization of large data sources, including both structured and unstructured data.
Experience leading or significantly contributing to the development of complex data solutions.
Basic idea or hands on with Tableau or similar data visualization tools/stacks.

Nice To Haves

Hands-on experience with MLOps, Git Version Control, Unit/Integration/End-to-End Testing, CI/CD, and release management processes.
Hands-on experience with Snowflake, JIRA or ServiceNow.
Design and implement data-grounded AI agents using large language models (LLMs) and specialized toolkits (e.g., LangChain, agent frameworks) to automate complex decision-making and data querying workflows.
Familiarity with project management principles and best practices.

Responsibilities

Design, build, and rigorously validate machine learning and statistical models (including regression, classification, clustering, and ensemble methods) for predictive and prescriptive analytics.
Use deep analytical skills and data science knowledge to address complex, real-world business challenges and drive measurable impact.
Analyze large amounts of information to discover critical trends and patterns. Apply the scientific method to design experiments, formulate hypotheses, and conduct rigorous testing.
Apply expertise in natural language processing (NLP) and text mining techniques where applicable.
Design, build, and maintain robust and scalable data pipelines to process, transform, and organize large, complex datasets from disparate sources.
Identify, assess, and integrate valuable data sources, developing automated processes for continuous data collection and ingestion.
Hands-on expertise in data management, programming, and processing large data volumes using technologies such as Python, SQL, and PySpark.
Undertake meticulous preprocessing, cleansing, and transformation of large structured and unstructured datasets to ensure data quality, usability, and accuracy for modeling.
Apply understanding of data management and data engineering principles to maintain scalable data architecture.
Design dimensional data models using methodologies to ensure enterprise data consistency.
Contribute to team efforts, including taking on new tasks as assigned by the supervisor.
Assist with special projects as needed to support departmental goals.
Handle cross-functional support duties, such as helping other departments with specific projects when required.