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# Data Engineer

- **Company:** Smarty, LLC
- **Location:** Orem, UT, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $90,000.0 - $120,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Amazon S3, Apache HTTP Server, Backup Devices, Software as a Service, Code Review, Data as a Services, Information Engineering, Extract Transform Load (ETL), Relational Databases, Document-Oriented Databases, Google Analytics, Identity and Access Management, Python (Programming Language), MySQL, Open Source Technology, Query Optimization, Power BI, Standard Sql, Salesforce.Com, Systems Integration, Tableau (Software), Data Logging, Data Storage Management, Data Ingestion, Data Build Tool (dbt), AWS Lambda, AWS ECS, Git, Cloudformation, AWS Glue, AWS Data Analytics, Bitbucket, Hubspot, Data Delivery, Cloudwatch, Looker Analytics, Software Version Control, Data Pipelines
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=232fc1791e50c6b0

## About the Role

* 3+ years of experience in a Data Engineering or similar role
 * Strong hands-on experience with dbt (data build tool) - modeling, incremental strategies, testing, and documentation
 * Proficiency in Python for data pipeline development and automation
 * Strong working knowledge of AWS data services, including:
*

 + Amazon S3 - storage management, partitioning, lifecycle
 + Amazon Athena - query optimization, DDL management
 + AWS Glue - data catalog, crawlers, DDL management
 + AWS Lambda - event-driven processing and automation
 + AWS Step Functions & EventBridge - pipeline orchestration and scheduling
 + AWS ECS/ECR - running containerized workloads
 + AWS CloudFormation - infrastructure as code
 + AWS CloudWatch - logging and monitoring
 + AWS IAM & Secrets Manager - permissions and credential management
 * Experience managing an Airbyte (or similar ELT tool such as Fivetran) deployment and its source connections
 * Solid SQL skills and comfort with data modeling concepts (staging, intermediate, fact/dimension layers)
 * Familiarity with MySQL or similar relational databases as data sources
 * Experience with version control (Git/Bitbucket or similar) and code review workflows
 * Strong problem-solving skills and a proactive approach to pipeline monitoring and data quality
 * Nice to have:
*

 + Experience integrating with SaaS APIs such as Salesforce, HubSpot, Google Analytics, or Help Scout
 + Familiarity with Power BI or other BI tools (Tableau, Looker, etc.)
 + Experience with reverse ETL patterns (pushing transformed data back to source systems)
 + Experience working in a SaaS company environment or with product usage/billing data
 + Familiarity with Apache Iceberg or similar table formats (VACUUM/OPTIMIZE operations)
 + AWS certifications (e.g., AWS Certified Data Engineer, Solutions Architect)

## Description

* Design, build, and maintain scalable data pipelines and ELT workflows using open-source/self-hosted Airbyte and custom Python
 * Develop and maintain dbt models, tests, and documentation to transform raw source data into reliable fact and dimension tables
 * Manage and optimize our AWS data lakehouse architecture, including S3 storage, Glue data catalog, and Athena query layer
 * Orchestrate automated data workflows using AWS Step Functions and EventBridge schedules
 * Deploy and manage Lambda functions for near-real-time pipelines, reverse ETL, and Slack-based data delivery
 * Manage infrastructure as code using AWS CloudFormation for Lambdas, Step Functions, and Glue resources
 * Maintain and administer Airbyte for data ingestion across sources such as HubSpot, MySQL databases, Google Analytics, and others
 * Write and maintain bespoke Python pipelines for sources and use cases not well-served by off-the-shelf connectors
 * Monitor pipeline health via AWS CloudWatch, respond to failures, and maintain data quality through dbt testing
 * Manage AWS services including ECS/ECR (containerized pipeline execution), Secrets Manager, SNS, and IAM
 * Collaborate with data analysts to support Power BI reporting, including scheduled semantic model refreshes
 * Document data architecture, pipelines, and operational runbooks to support team knowledge and continuity


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- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)

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