> Markdown version of [/jobs/ext/959611-lead-data-ai-engineering](https://www.wearedevelopers.com/jobs/ext/959611-lead-data-ai-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).

---

# Lead Data/AI Engineering

- **Company:** AT&T Inc.
- **Location:** Dallas, TX, United States
- **Experience:** Expert
- **Salary:** $158,200.0 - $237,400.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Microsoft Azure, Big Data, Data Architecture, Information Engineering, Data Infrastructure, Data Systems, Data Warehousing, Apache Hadoop, Python (Programming Language), Machine Learning, Scala (Programming Language), Unstructured Data, Data Storage Technologies, Data Ingestion, Sql Optimization, Snowflake, Apache Spark, Pyspark, Information Technology, Apache Kafka, Data Management, Data Pipelines, Databricks, Programming Languages
- **Published:** June 13, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=4988e5802781ffb3

## About the Role

Do you have experience in Databricks?, REQUIREMENTS: Requires a Bachelor's degree, or foreign equivalent degree in Electrical and Electronic Engineering, Computer Science, or Information Technology and five (5) years of progressive, post-baccalaureate experience in the job offered or five (5) years of progressive, post-baccalaureate experience in a related occupation utilizing programming languages (Python, PySpark, and Scala); using data warehousing solutions including Snowflake; utilizing advanced SQL skills; working with Databricks within Azure cloud environments; and employing big data technologies, including Apache Spark, Hadoop, and Kafka, and data science concepts and machine learning.

## Description

DUTIES: Design, develop, and maintain scalable and efficient data pipelines utilizing advanced technologies such as Python, PySpark, and Databricks. Integrate data from diverse sources while ensuring high standards of data quality, consistency, and reliability. Formulate and implement comprehensive data architecture strategies, encompass data modeling, schema design, and data storage solutions, as well as optimizing data processing workflows for enhanced performance, scalability, and cost-efficiency. Collaborate with data scientists, analysts, and stakeholders is essential to accurately understand data requirements and deliver tailored data solutions. Identify and resolve data-related issues, support data infrastructure, and maintain detailed documentation of all data pipelines, architecture, and processes. Drive the design and implementation of data models to enhance business decision-making by generating insights from both internal and external data assets. Define data requirements, mine
and validate large-scale structured and unstructured datasets using cloud-based tools and supporting both standard and customized data analyses. Develop robust mechanisms for data ingestion, analysis, validation, normalization, and cleaning alongside upholding best practices in data engineering and contributing to advanced data analytics and visualization initiatives. Utilize programming languages (Python, PySpark, and Scala). Use data warehousing solutions including Snowflake. Utilize advanced SQL skills. Work with Databricks within Azure cloud environments. Employ big data technologies, including Apache Spark, Hadoop, and Kafka, and data science concepts and machine learning.


## Related Videos

- [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data)
- [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology)
- [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)
- [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)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [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)
- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [Top-Paying Tech Jobs (with Salaries)](https://www.wearedevelopers.com/magazine/372-top-paying-tech-jobs-with-salaries)
- [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)