Data Engineer I

Destination Homes•Sandy, UT

17h•Onsite

About The Position

The Data Engineer I is an entry-level member of the Data & Analytics team who helps build and support reliable data pipelines and analytics-ready datasets. This position works with data from applications, databases, APIs, files, and third-party sources and helps transform that data into trusted, documented, and secure data products in Snowflake. The Data Engineer I works under the guidance of experienced data engineers and the Director of Data and Analytics. The position is responsible for learning and applying sound data-engineering practices, including SQL and Python development, data modeling, testing, monitoring, documentation, source-system integration, and production support. The successful candidate is curious, detail-oriented, comfortable learning new technologies, and able to explain technical issues clearly to both technical and business audiences.

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 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 concepts, including ingestion, transformation, loading, incremental processing, and data validation.
Familiarity with REST APIs, JSON, CSV files, 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.

Nice To Haves

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.

Responsibilities

Protect the legal, financial, and moral well-being of LHM and its portfolio companies.
Support the reliability, accuracy, security, and usability of data used for reporting, analytics, applications, and business decision-making.
Learn from and contribute to the success of other members of the Data & Analytics team.
Seek 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.
Pull data from databases, APIs, cloud storage, flat files, and other approved sources into Snowflake or related data platforms.
Organize data into appropriate raw, staged, and curated layers using established team standards.
Develop reusable, parameterized, and maintainable pipeline components rather than one-time manual processes.
Create and maintain analytics-ready tables, views, data marts, and dimensional models for 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, including reviewing logs, identifying root causes, documenting incidents, and escalating issues 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.
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 business stakeholders to understand requirements and deliver reliable data.
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 and learn new technologies as the LHM data platform evolves.
Perform other duties as assigned.