Data Engineer I

The Larry H. Miller Company Data & Analytics team is seeking an entry-level Data Engineer I to help build and support dependable data pipelines and analytics-ready datasets. This onsite role in Sandy, UT focuses on transforming data from multiple sources into trusted, documented, and secure data products built in Snowflake.

In this position, you will contribute to reliable reporting and analytics by developing ingestion and transformation pipelines, creating curated data layers, and partnering with analysts and business stakeholders to apply consistent business rules and definitions.

What you’ll do

• Support the reliability, accuracy, security, and usability of data for reporting, analytics, applications, and business decision-making.
• Build, maintain, and support data ingestion and transformation pipelines using SQL, Python, and approved data-engineering tools.
• Pull data from databases, APIs, cloud storage, flat files, and other approved sources into Snowflake or related data platforms.
• Organize data into raw, staged, and curated layers using established team standards.
• Create reusable, parameterized, and maintainable pipeline components to avoid one-time manual processes.
• Develop and maintain analytics-ready tables, views, data marts, and dimensional models to support reporting and analysis.
• Apply business rules, transformations, calculations, and standard definitions with analysts and business stakeholders.
• Implement data-quality checks for completeness, accuracy, duplicates, nulls, referential integrity, valid values, and unexpected changes in record volumes.
• Reconcile data between source systems and the data platform, investigate discrepancies, and document findings.
• Schedule, monitor, and troubleshoot data jobs by reviewing logs, identifying root causes, documenting incidents, and escalating when appropriate.
• Support pipeline alerts, retries, restart procedures, and other reliability practices.
• Document data sources, refresh schedules, transformations, dependencies, ownership, and known limitations.
• Create technical specifications, process diagrams, test plans, and deployment documentation.
• Write and maintain unit, integration, regression, and data-validation tests, and document test results.
• Use Git, pull requests, code reviews, and established development, test, and production promotion practices.
• Assist with production support and participate in on-call or after-hours support when required by team practices.
• Stay current with modern data-engineering practices as the LHM data platform evolves.
• Follow LHM policies, data-security practices, coding standards, and change-management processes.

Security, compliance, and governance

• Follow security and governance practices for sensitive financial, employee, customer, health, and other restricted data, including appropriate access controls and data handling.
• Protect the legal, financial, and moral well-being of LHM and its portfolio companies.
• Demonstrate ethical behavior and professionalism consistent with LHM standards, including responsible handling of company and customer data.

Technologies you’ll work with

• SQL, Python, Snowflake
• Git
• REST APIs, JSON, CSV
• ETL, ELT, dbt
• Control-M, Airflow, Prefect
• AWS services including S3, Lambda, Glue, Secrets Manager, SNS
• Power BI
• CI/CD, Docker, Linux
• Data cataloging, metadata, lineage, and data-governance
• Dimensional data-modeling

Qualifications

• Bachelor’s degree in computer science, information systems, data engineering, data analytics, engineering, or a related field preferred; equivalent education, training, internship, project, or work experience may be considered.
• Zero to two years of experience in data engineering, software development, analytics engineering, database development, or a related field (relevant academic, internship, or portfolio projects may qualify).
• Demonstrated experience completing a data, programming, database, or automation project from requirements through testing and documentation.
• Strong foundation in SQL, including joins, common table expressions, aggregations, window functions, and basic query troubleshooting.
• Working knowledge of Python or another modern programming language, with the ability and willingness to develop in Python.
• Understanding of relational databases, data types, keys, normalization, and basic dimensional data-modeling concepts.
• Understanding of ETL and ELT, including ingestion, transformation, loading, incremental processing, and data validation.
• Familiarity with REST APIs, JSON, CSV, or other common data-integration formats.
• Familiarity with Git, source control, testing, debugging, and code documentation.
• Strong analytical and problem-solving skills, including the ability to investigate unexpected data results.
• Strong attention to detail and commitment to data accuracy, security, and reliability.
• Ability to communicate clearly in writing and verbally, and adjust technical explanations for different audiences.
• Ability to work collaboratively, accept feedback, manage priorities, and ask for help when appropriate.

Preferred knowledge

• Experience with Snowflake or another cloud data warehouse.
• Experience with dbt, Control-M, Airflow, Prefect, or another workflow-orchestration tool.
• Experience with AWS services such as S3, Lambda, Glue, Secrets Manager, or SNS.
• Experience with Power BI or another business-intelligence platform, especially data models and semantic layers.
• Familiarity with CI/CD, Docker, Linux, cloud security, data cataloging, metadata, lineage, or data-governance practices.
• Familiarity with financial, ERP, HR, ticketing, sports, real estate, health-care, or other operational data.
• Basic understanding of accounting concepts such as general ledger, debits and credits, trial balance, and financial statements.

Reporting line

• Reports to: Director of Data and Analytics

Work setting

• Primarily office setting.
• Regularly required to sit, stand, bend, reach, and move about facilities.
• May be required to perform other duties as assigned.