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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer IV - AI & Data Products - **Company:** Acima Leasing - **Location:** Draper, UT, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Code Generation, Data Validation, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Transformation, Data Systems, Data Warehousing, Linux, Digital Assets, Python (Programming Language), Metadata, Standard Sql, SQL Databases, Data Classification, Sql Optimization, Snowflake, Generative AI, Performance Monitor, Data Delivery, Restful APIs, Data Pipelines - **Published:** May 20, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=95f67a199a0b8bdb ## About the Role Do you have experience in SQL databases?, * 8+ years of experience in Data Engineering roles * 2+ years of warehouse data modeling and pipeline design experience * Experience with data warehousing: Snowflake or similar systems * Strong Python experience * Strong SQL experience * Strong REST API experience * Experience using Linux. * Ability and motivation to learn new technologies quickly with minimal support and guidance. * Strong communication skills * Experience supporting and working with cross-functional teams in a dynamic environment., * AI-Driven Data Engineering Mindset - Hands-on experience using AI-assisted development tools (e.g., Copilot, generative AI) to accelerate pipeline design, code generation, and troubleshooting. * Intelligent Pipeline Development - Ability to design pipelines that incorporate AI for data transformation, anomaly detection, data classification, and schema evolution. * AI-Enabled Data Quality & Observability - Experience using AI/ML techniques for automated data quality checks, root cause analysis, and proactive monitoring. Metadata & Semantic Layer Enrichment - Ability to use AI to auto-generate metadata, data documentation, semantic mappings, and business glossary entries ## Description Data Products & Data Integration * Define, build, validate and maintain domain-specific data product assets and data pipelines. * Collaborate with analytics and business partners to define and build semantic layers/models to support various personas - Report consumers, Self-Service and Advanced SQL Analysts, Data Scientists and AI use cases/agents. * Create and maintain data domain assets (pipelines/ETLs, sematic models and other assets) using Python, SQL, and other approved tools. * Build high quality solutions and assets aligned to defined architectural guidelines, data integration patterns, frameworks, and tools. * Build solutions aligned to security, risk, and compliance guidelines. * Support analysts and product teams with data products and assigned data domain expertise. * Assemble large complex data sets that meet functional/non-functional business requirements. * Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery and re-designing infrastructure for greater scalability. * Perform data analysis required to troubleshoot data related issues and assist in the resolution of data issues. Data Governance & Quality * Manage business glossary in collaboration with business teams and build/maintain technical metadata for assigned data domains. * Define data quality rules and controls for assigned data domains and data assets (consumption, gold, silver, bronze layer tables). * Validate domain datasets for accuracy and completeness. Ensure domain data is of high quality and consumable by reporting, analytics, data science, and AI use cases. Stakeholder Collaboration * Collaborate with business leaders, Executives, Data Scientists, BI Analytics, Product, Engineering, and other operational departments to ensure successful delivery of data solutions and data assets (consumption, gold, silver, bronze layer tables) * Translate business requirements into Data Products and data assets (consumption, gold, silver, bronze layer tables & pipelines) build specifications. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [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) - [Docker exec without Docker](https://www.wearedevelopers.com/videos/1094-docker-exec-without-docker) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)