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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** QGENDA, LLC - **Location:** Atlanta, GA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Architectural Patterns, Microsoft Azure, BigQuery, Code Review, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Structures, Data Systems, Data Visualization, Software Debugging, Decision Support Systems, Distributed Computing Environment, Data Flow Control, Python (Programming Language), Metadata, Natural Language Processing, Performance Tuning, Power BI, DataOps, SQL Databases, Tableau (Software), Data Processing, Google Cloud, Snowflake, Git, Cloudformation, Data Layers, Infrastructure Automation Frameworks, Data Lineage, Terraform, Looker Analytics, Software Version Control, Data Pipelines, Amazon Redshift - **Published:** July 17, 2026 - **Apply:** https://www.dice.com/job-detail/0bf55eab-c535-4052-90c0-b230afd02886 ## About the Role * Exceptional analytical, problem solving, and debugging skills * Strong communication with the ability to simplify and articulate technical concepts * Ability to work collaboratively, influence architecture, and take ownership of deliverables * Commitment to quality, reliability, and continuous improvement Experience You Bring * 5-7+ years in data engineering/analytics engineering, or related field * Bachelor's degree specializing in computing, data engineering, or related discipline * Expertise in distributed data processing, data modeling, and performance tuning * Strong proficiency in SQL and Python * Experience with modern data stack components, such as: + Cloud: AWS, Google Cloud Platform, Azure + Warehouses: Snowflake, Redshift, BigQuery, etc. + Orchestration: Airflow, MWAA, Composer, etc. + Transformation: dbt, etc. + Observability: data lineage/monitoring tools + BI: Looker, Tableau, Power BI, etc. + DevOps: Git, CI/CD, Terraform/CloudFormation Not Required, But Nice to Have * Experience preparing datasets and data structures for AI/ML use cases, including NLP-driven analytics * Experience with Glue, Dataflow ## Description As a Senior Data Engineer, you will design, build, and optimize the data platform, including pipelines, models, and infrastructure that power analytics, reporting, and data-driven decision making across the QGenda product lines. You will serve as a technical leader with the team, contributing to architectural direction, driving best practices, and supporting complex data initiatives. This role requires deep technical expertise, strong cross-functional collaboration, and the ability to deliver scalable, high-performing data systems that meet evolving business needs. How You'll Make an Impact Deliver High-Quality, Scalable Data Engineering Solutions * Architect, develop, test, and maintain ELT/ETL pipelines and data workflows supporting high-volume analytics * Implement advanced data processing solutions and observability techniques to ensure data is accurate, fresh, and reliable * Design and refine data models and semantic layers that support analytical self-service and advanced reporting. * Build data visualizations and dashboards supporting analytics use cases Strengthen Data Engineering Practices and Technical Standards * Translate complex business and analytics requirements into efficient, scalable data solutions * Apply best practices for version control, documentation, CI/CD, Infrastructure as Code, and data governance * Participate in code reviews, identify opportunities for architectural improvement,, and contribute to continuous improvement efforts Collaborate Across Teams * Partner with data engineers, DBAs, managers, and business stakeholders to deliver high-impact data products * Provide technical guidance, informal mentorship, and support to other engineers in order to elevate team capabilities * Communicate technical decisions, risks, and recommendations to both technical and non-technical audiences Drive Technical Excellence * Optimize data pipelines and warehouse performance for speed, cost, and scalability * Evaluate, prototype, and influence adoption of new tools, frameworks, and architectural patterns that enhance the data platform * Contribute to data observability, incident response, and root-cause analysis for complex data issues * Design and deliver AI-ready data products, ensuring data structures, metadata, and pipelines are suitable for natural language processing, predictive analytics, and other AI-driven capabilities ## Related Videos - [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) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Got AI ideas but no money? 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