> Markdown version of [/jobs/ext/1330911-senior-analyst-data-engineering](https://www.wearedevelopers.com/jobs/ext/1330911-senior-analyst-data-engineering). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior Analyst, Data Engineering

- **Company:** Dell Technologies Inc.
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Salary:** $102,000.0 - $132,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Data Analysis, Business Logic, Big Data, Profiling, Computer Programming, Databases, Continuous Integration, Data Architecture, Data Validation, Data Cleansing, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Data Integrity, Extract Transform Load (ETL), Data Transformation, Data Migration, Data Systems, Data Visualization, Database Storage Structures, Github, Python (Programming Language), PostgreSQL, Machine Learning, Meta-Data Management, Online Analytical Processing, Productivity Software, Power BI, Cloud Services, DataOps, Search Technologies, SQL Databases, SQL Server Reporting Services, SQL Server Integration Services, Teradata SQL, Unstructured Data, Feature Engineering, Large Language Models, Prompt Engineering, Git, Data Lineage, Data Management, Machine Learning Operations, Virtual Agents, Software Version Control, Data Pipelines
- **Published:** July 18, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=9620442cd266fbc0

## About the Role

Databases & SQL: Proficiency in Teradata, PostgreSQL, and SQL for querying, transforming, profiling, and validating data, with a strong understanding of relational, dimensional, and analytical data models to accurately map source-to-target schemas
ETL/ELT & Development Practices: Experience with Informatica, Apache Airflow, or comparable data integration platforms, along with familiarity with version control and CI/CD practices using Git-based development workflows
Programming & Automation: Proficiency in Python for automation, orchestration, and custom data solutions, with the ability to manage unexpected data quality issues, platform constraints, and migration challenges with agility
Data Quality & Governance: Understanding of data lineage, metadata management, master data management (MDM), and governance concepts to ensure data integrity and compliance throughout the data lifecycle
AI-Ready Data Engineering: Knowledge of data preparation, feature engineering concepts, and dataset management for AI/ML workloads, enabling trusted and well-governed datasets for advanced analytics and model development
Desirable Requirements:
Analytics, Reporting & Modern Data Platforms: Knowledge of Power BI or other visualization tools, experience with Airflow, enterprise schedulers, SSIS, SSRS, and Tabular OLAP/semantic modeling, along with understanding of MLOps, DataOps, cloud-native data services, modern lakehouse architectures, and data observability/automated anomaly detection solutions
AI & Intelligent Automation: Exposure to machine learning and AI technologies for automation and operational efficiency, including familiarity with Agentic AI concepts (LLMs, prompt engineering, vector databases, semantic search, responsible AI/AI governance), experience using AI-powered productivity tools such as GitHub and Devin, and understanding of modern AI-driven development practices

## Description

Data Engineering is the practice of designing, building, and maintaining systems that collect, store, process, govern, and analyze large volumes of data required by analysts, data scientists, and AI/ML applications. It serves as the foundation for enabling data-driven insights, intelligent automation, and AI-powered decision-making across the organization.
What You'll Achieve
Data Management, Engineering & AI Enablement - ETL/ELT processing, data transformation, data quality, governance, security, and AI-ready data architectures across enterprise platforms. You will help enable trusted, high-quality data to support analytics, reporting, machine learning, agentic AI solutions, and operational decision-making.
You Will:
Data Migration & Mapping: Analyze source and target database structures, identify data dependencies, constraints, and transformation needs, and create source-to-target mapping documents with defined transformation rules and business logic in collaboration with stakeholders
Data Pipeline Design & Architecture: Work with structured and unstructured data to design and implement scalable data pipelines that support analytics, AI, and machine learning workloads, aligning schemas, relationships, and data models with data architects
Data Quality & Governance: Develop processes to improve data quality, observability, lineage, and governance while ensuring data platforms comply with enterprise security, privacy, and responsible AI standards
AI/ML Enablement & Agentic AI Support: Partner with data scientists, AI engineers, and business teams to enable trusted datasets for AI/ML model development and support implementation of data solutions for Agentic AI use cases
AI-Driven Development & Automation: Leverage AI-assisted development tools to improve productivity and documentation quality, and evaluate opportunities for intelligent automation using AI and machine learning techniques within data engineering processes


## 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)
- [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next)
- [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)
- [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models)

## 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)
- [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)
- [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)
- [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)
- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)