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 elevate 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 #J-18808-Ljbffr Larry H. Miller Company