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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ETL Engineer - **Company:** WorldStrides, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Audit Trail, Automation of Tests, Business Intelligence Development, Code Review, Databases, Continuous Integration, Data Architecture, Information Engineering, Data Infrastructure, Data Integrity, Extract Transform Load (ETL), Data Warehousing, Relational Databases, IBM InfoSphere DataStage, Programming Tools, Fault Tolerance, Data Flow Control, Python (Programming Language), Metadata, Meta-Data Management, Microsoft Office, Performance Tuning, Query Optimization, DataOps, SQL Databases, Data Streaming, EHR Systems, AI Coding Agents, Agentic-AI, Microsoft Fabric, Information Technology, Data Lineage, Data Management, Software Coding, Data Pipelines - **Published:** October 15, 2026 - **Apply:** https://www.builtincolorado.com/job/senior-etl-engineer/11687698?handler=ApplyRedirect ## About the Role * Advanced experience with SQL, relational databases, and data warehouse design * Strong expertise with Microsoft Fabric and modern data platform concepts * Proficiency in Python for ETL development, automation, and testing * Deep understanding of data modeling, query optimization, and performance tuning * Strong knowledge of DataOps, CI/CD, and automated testing for data platforms * Experience designing and supporting production-grade, enterprise-scale pipelines * Hands-on production experience with AI coding assistants and agentic development tools, plus familiarity with MCP and agent tool integration * A track record of delivering faster with AI without sacrificing quality; examples of your work are welcome * Enough working knowledge of the AI tooling landscape to make pragmatic choices, without chasing every new model or tool * Strong code review skills, especially spotting subtle logic, security, and performance issues in generated code * Experience engaging stakeholders early to shape requirements, not just executing against them * Excellent verbal, written, and interpersonal communication skills * Proven ability to lead multiple initiatives and influence without formal authority * Preferred: experience with metadata- or config-driven pipeline frameworks. Education & Experience * 5+ years of experience in data engineering, BI development, or analytics engineering * Bachelor's degree in a technical or analytical discipline required ## Description Designs and stewards enterprise ETL/ELT pipelines, lakehouse, warehouse, and data models using Microsoft Fabric, SQL, and Python. Establishes data quality, testing, lineage, security, governance, DataOps, CI/CD, and performance optimization practices. Uses AI coding and agentic tools for development while reviewing outputs rigorously. Partners with stakeholders to shape requirements, leads complex initiatives, mentors engineers, and collaborates with analysts, data scientists, and BI teams to deliver reliable data products., The Senior ETL Engineer designs, builds, and stewards the enterprise data pipelines, lakehouse, and warehouse solutions behind reporting, analytics, and advanced data use cases. They work with an AI-first mindset. AI agents handle much of the hands-on execution, such as code generation, test scaffolding, documentation, and troubleshooting, while the engineer owns the design, the specification, and the review. Beyond execution, AI is a thinking partner for architecture, trade-off analysis, and connecting the dots across systems. Just as important, this engineer knows when AI isn't the right tool, and holds AI-produced work to the same standard as their own. This role partners with the business from problem framing through delivery. Rather than executing tickets in isolation, the engineer engages stakeholders early to explore what's possible and shape requirements, applying AI to discovery as well as execution. Key customers include business stakeholders, analysts, and BI teams. Data Engineering * Design, build, and maintain scalable, high-performance ETL/ELT pipelines supporting analytics, reporting, and advanced data use cases * Help shape and consistently apply data engineering standards, best practices, and reusable patterns, in partnership with the data architect and senior engineers * Lead data modeling efforts to ensure datasets are optimized for analytics, performance, and usability * Evaluate and recommend improvements to existing data architectures, tools, and processes to support growth, scalability, and cost efficiency AI-First Engineering * Use AI coding assistants and agentic tools as the default for pipeline development, config authoring, test generation, and documentation, shifting personal effort from writing code to specifying, reviewing, and validating it * Break work into clear, well-scoped specifications that agents can execute reliably, and review AI-generated output with the same rigor as a peer's pull request * Judge when AI is the right tool, and when deterministic automation, templating, or hands-on work is faster, safer, or more reliable * Use AI as a thinking partner for architecture decisions, design reviews, root-cause analysis, and pressure-testing assumptions before committing to an approach * Contribute to and help steward the team's shared AI toolkit, including agent skills, reusable prompts, context and convention files, and MCP-connected tooling, so each project makes the next one faster * Write documentation and standards that both engineers and AI agents can read and use Consultative Engagement * Engage business stakeholders early, often before a ticket exists, to explore what's possible at a conceptual and logical level and to shape high-level requirements together * Balance consultation with delivery: right-size discovery to the problem, and know when to keep exploring versus when to commit and build * Use AI to make early conversations concrete, such as quickly prototyping data models, mocking sample outputs, or sketching options for stakeholders to react to * Use AI to turn discovery conversations into draft requirements, flag gaps and ambiguities, and generate follow-up questions before work begins * Offer alternatives and push back constructively when a request doesn't fit the underlying business need, explaining trade-offs in business terms Data Quality, Reliability & Operations * Establish and apply data quality, validation, fault-tolerance, and automated testing frameworks across all pipeline stages * Design testing approaches where engineers define expectations and data contracts, and agents generate and run tests against them * Ensure data integrity, accuracy, freshness, and lineage throughout the data lifecycle * Use AI-assisted anomaly detection and troubleshooting to shorten time to resolution, while verifying root causes before acting DataOps, Automation & Optimization * Contribute to DataOps and CI/CD practices for data pipeline deployment and management * Drive automation to reduce manual processes in all stages of development * Continuously optimize pipeline performance through query tuning, architectural improvements, and technology enhancements * Identify and eliminate waste in data flows, reducing redundant processing and improving overall efficiency * Adopt new tools and practices deliberately, favoring depth and standardization over tool churn Leadership & Mentorship * Lead complex or high-impact initiatives as assigned, collaborating on architecture with the data architect * Mentor and coach ETL Engineers, including on effective AI-assisted workflows: writing good specs, reviewing generated output critically, and recognizing where AI falls short * Model AI-first habits in code reviews, design discussions, and documentation * Share knowledge through documentation, design artifacts, and technical guidance Collaboration & Stakeholder Engagement * Lead technical discussions to resolve ambiguous or competing data needs * Communicate complex concepts clearly to technical and non-technical audiences * Collaborate with data scientists, analysts, and BI engineers to adopt data products and improve data usability and trust * Build data products that AI agents and AI-assisted analysts can consume reliably, through well-defined semantic models and query contracts * Act as a trusted advisor on data feasibility, trade-offs, risks, and prioritization Security, Governance & Compliance * Ensure data pipelines adhere to enterprise security, privacy, and governance standards * Implement access controls, data protection measures, and compliance requirements * Apply guardrails to AI and agent usage, including tool permission policies, human approval for writes and pipeline execution, and keeping sensitive customer and financial data within sanctioned tools * Support data lineage, auditability, and metadata management initiatives, Provides technical leadership and hands-on development for Informatica ETL and enterprise data warehouse implementations. Designs, develops, tests, supports, and optimizes workflows, mappings, data integration processes, and dimensional models. Maintains platform health, resolves technical issues, leads releases, establishes development standards, supports data quality and governance, and collaborates with QA and business teams. Requires strong SQL, cloud, Kimball modeling, data management, and Informatica expertise, plus occasional on-call duty and travel. Top Skills: Cloud ComputingEnterprise Data WarehousingETLInformatica IdqInformatica PowercenterInformatica Web Services HubKimball Dimensional ModelingSQL PingWind, Design, develop, maintain, and optimize enterprise ETL pipelines supporting Army Human Resources Command data. Build data mappings and transformation logic, ensure data quality and security compliance, maintain database systems, manage scheduling and monitoring for reliable pipelines, provide lifecycle technical solutions, and create architecture and data-flow documentation using DataStage and SQL databases. Top Skills: HTMLIbm Infosphere DatastageMicrosoft Sql ServerMicrosoft VisioOraclePostgresSQLXML Wipfli, Manage healthcare revenue cycle activities including billing, collections, claims, accounts receivable, denials, payer follow-up, reconciliations, and reimbursement analysis. Support outsourced client engagements, improve operational workflows, communicate with clients, and provide guidance on revenue cycle best practices. Use EHR systems, payer portals, clearinghouses, and Microsoft tools while coordinating domestic and offshore teams and managing multiple client engagements. 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