Data Engineer I

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

Role Responsibilities

• Support the reliability, accuracy, security, and usability of data used for reporting, analytics, applications, and business decision-making.
• Protect the legal, financial, and moral well-being of LHM and its portfolio companies.
• Learn from and contribute to the success of other members of the Data & Analytics team.
• Identify opportunities to improve data processes, reduce manual work, and increase operational efficiency.
• Follow LHM policies, data-security practices, coding standards, and change-management processes.
• Build, maintain, and support data ingestion and transformation pipelines using SQL, Python, and approved data-engineering tools.
• Ingest 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 instead of 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 in partnership 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 and investigate discrepancies.
• Schedule, monitor, and troubleshoot data jobs by reviewing logs, identifying root causes, documenting incidents, and escalating issues when appropriate.
• Support reliability practices such as pipeline alerts, retries, and restart procedures.
• 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.
• Participate in technical design discussions and contribute to continuous improvement of the team’s data-engineering framework.
• Assist with Snowflake performance and cost optimization by improving queries, models, workloads, and data structures.
• Follow security and governance practices for sensitive financial, employee, customer, health, and other restricted data, including appropriate access controls and data handling.
• Collaborate with data analysts, BI developers, application teams, finance, HR, and other stakeholders to understand requirements and deliver reliable data.
• Support production operations and participate in on-call or after-hours support when required by team practices.
• Stay current with modern data-engineering practices and learn new technologies as the LHM data platform evolves.
• Perform other duties as assigned.

Required Qualifications

• Bachelor’s degree in computer science, information systems, data engineering, data analytics, engineering, or a related field is 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 SQL foundation, 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 concepts, including ingestion, transformation, loading, incremental processing, and data validation.
• Familiarity with REST APIs and common data-integration formats such as JSON and CSV.
• 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 with a 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.

Technologies and Tools

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

Preferred Knowledge and Skills

• 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

Location and Work Setting

Location: Sandy, UT (onsite)

Physical Requirements

• Work is performed primarily in an office setting.
• Regularly required to sit, stand, bend, reach, and move about facilities.
• May be required to perform other duties as assigned.

Education Preference

Preferred: Bachelor’s or better in Information Technology or related field