Data Engineer I

Entry-level role on the Data & Analytics team focused on building and supporting reliable data pipelines and analytics-ready datasets.

• Protect the legal, financial, and moral well-being of LHM and its portfolio companies
• Support reliability, accuracy, security, and usability of data used for reporting, analytics, applications, and business decision-making
• Improve data processes by reducing manual work and increasing 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 platforms
• Organize data into raw, staged, and curated layers aligned with established team standards
• Create reusable, parameterized, maintainable pipeline components instead of one-time manual processes
• Develop and maintain analytics-ready tables, views, data marts, and dimensional models for reporting and analysis
• Apply business rules 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 record-volume changes
• Reconcile data between source systems and the data platform, investigate discrepancies, and document findings
• Schedule, monitor, and troubleshoot data jobs: review logs, identify root causes, document incidents, and escalate as 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; 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 handling
• Collaborate with data analysts, BI developers, application teams, finance, HR, and other stakeholders to 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 as the LHM data platform evolves
• Perform other duties as assigned

Requirements

• 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 ability to deliver 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 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 and data formats such as JSON and CSV (or other common integration formats)
• Familiarity with Git, source control, testing, debugging, and code documentation
• Strong analytical and problem-solving skills, including investigating unexpected data results
• Strong attention to detail with commitment to data accuracy, security, and reliability
• Ability to communicate clearly in writing and verbally and tailor technical explanations to different audiences
• Ability to work collaboratively, accept feedback, manage priorities, and ask for help when appropriate

Technologies

• 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 other business-intelligence platforms, especially data models and semantic layers
• Familiarity with CI/CD, Docker, Linux, cloud security, data cataloging, metadata, lineage, or data-governance practices
• Familiarity with operational data such as financial, ERP, HR, ticketing, sports, real estate, or health-care
• Basic understanding of accounting concepts such as general ledger, debits and credits, trial balance, and financial statements

Education (additional)

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

Reporting

• Reports to: Director of Data and Analytics

Work location and environment

• Onsite role in Sandy, UT
• Physical requirements: primarily office setting; regularly required to sit, stand, bend, reach, and move about facilities; may be required to perform other duties as assigned