Cloud Data Engineer Jobs in Texas
Cloud Data Engineer jobs in Texas are among the most active in the country, concentrated in energy analytics, financial services, healthcare informatics, and enterprise technology with openings at every level from junior to principal engineer. Austin, Dallas, and Houston account for the bulk of hiring, where companies like Dell Technologies, ExxonMobil, and JPMorgan Chase have deep and lasting cloud data engineering teams. The most in-demand specialties are data pipeline architecture, cloud-native warehousing on platforms like Snowflake and BigQuery, and real-time streaming with Apache Kafka. Find a role that fits below and apply directly.
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As a Senior Lead Software Engineer at JPMorganChase within Corporate Technology – Chief Technology Office, you serve as a senior individual contributor and technical leader on an agile team designing and delivering trusted, market-leading technology products in a secure, stable, and scalable way. You drive critical technology solutions across multiple technical areas, translate firmwide objectives into concrete technical designs, and raise the engineering bar through strong architecture, high-quality delivery, and operational rigor.
Job responsibilities
- Serves as a hands-on technical leader, contributing production code (primarily Python) and owning end-to-end delivery from design through production operations.
- Leads system and platform architecture for components supporting end-to-end ML workflows, including data transformation patterns, feature management integration, orchestration enablement, and model serving integration.
- Designs and develops agentic AI capabilities that analyze workloads and generate optimization recommendations, including evaluation approaches, monitoring, and feedback loops required for production readiness.
- Builds and maintains reusable APIs/SDKs and reference implementations that enable consistent platform adoption and reduce duplicated effort across teams.
- Partners with data scientists, ML engineers, and product teams to clarify requirements, define technical approaches, manage dependencies, and deliver measurable outcomes.
- Ensures platform reliability, scalability, performance, and cost efficiency through SLOs, proactive monitoring, incident response participation, root-cause analysis, and continuous improvement.
- Automates infrastructure provisioning, configuration, and CI/CD pipelines for platform services using Infrastructure as Code, promoting safe and repeatable deployments across environments.
- Drives adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, establishing consistent validation standards (correctness, performance, security) and promoting reuse of effective patterns.
- Produces architecture and design artifacts for platform components, ensuring alignment with enterprise standards and best practices.
- Mentors engineers through technical coaching, design reviews, and pairing, contributing to a strong culture of engineering excellence.
- Contributes to software engineering communities of practice and events that explore new and emerging technologies.
Required qualifications
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- Demonstrated experience in system design, application development, testing, and operational stability for distributed systems and platform services.
- Strong hands-on depth in Python, including building and maintaining APIs/SDKs; ability to lead design and perform high-quality code/design reviews.
- Demonstrated experience with enterprise-authorized AI-assisted development tools (e.g., GitHub Copilot, Claude Code) and experience establishing practical team norms for validation and quality (correctness, performance, security) in day-to-day engineering workflows.
- Experience delivering platforms involving Generative AI, including LLM orchestration patterns and agentic AI frameworks, with production-grade evaluation and monitoring practices.
- Practical experience with AWS, containerized platforms (e.g., EKS/ECS), and Terraform (or equivalent IaC).
- Databricks experience is required, including building and operating data/ML workloads on Databricks (e.g., pipelines, notebooks/jobs, Delta/feature datasets, orchestration/integration with ML workflows).
- Comprehensive knowledge of the Software Development Life Cycle, agile delivery, CI/CD, and modern engineering quality controls.
- Strong communication skills with business-facing partners and technical stakeholders; ability to translate strategy into execution and measurable outcomes.
Preferred qualifications
- Experience building ML platforms integrating data engineering, feature management, and model serving into cohesive developer-friendly workflows.
- Knowledge of AI/ML model integration, context engineering, and MCP-style patterns for tool/function integration.
- Exposure to Snowflake.
- Familiarity with observability/metrics tools (e.g., CloudWatch, Dynatrace, Datadog).
- AWS certifications.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
As a Senior Lead Software Engineer at JPMorganChase within Corporate Technology – Chief Technology Office, you serve as a senior individual contributor and technical leader on an agile team designing and delivering trusted, market-leading technology products in a secure, stable, and scalable way. You drive critical technology solutions across multiple technical areas, translate firmwide objectives into concrete technical designs, and raise the engineering bar through strong architecture, high-quality delivery, and operational rigor.
Job responsibilities
- Serves as a hands-on technical leader, contributing production code (primarily Python) and owning end-to-end delivery from design through production operations.
- Leads system and platform architecture for components supporting end-to-end ML workflows, including data transformation patterns, feature management integration, orchestration enablement, and model serving integration.
- Designs and develops agentic AI capabilities that analyze workloads and generate optimization recommendations, including evaluation approaches, monitoring, and feedback loops required for production readiness.
- Builds and maintains reusable APIs/SDKs and reference implementations that enable consistent platform adoption and reduce duplicated effort across teams.
- Partners with data scientists, ML engineers, and product teams to clarify requirements, define technical approaches, manage dependencies, and deliver measurable outcomes.
- Ensures platform reliability, scalability, performance, and cost efficiency through SLOs, proactive monitoring, incident response participation, root-cause analysis, and continuous improvement.
- Automates infrastructure provisioning, configuration, and CI/CD pipelines for platform services using Infrastructure as Code, promoting safe and repeatable deployments across environments.
- Drives adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, establishing consistent validation standards (correctness, performance, security) and promoting reuse of effective patterns.
- Produces architecture and design artifacts for platform components, ensuring alignment with enterprise standards and best practices.
- Mentors engineers through technical coaching, design reviews, and pairing, contributing to a strong culture of engineering excellence.
- Contributes to software engineering communities of practice and events that explore new and emerging technologies.
Required qualifications
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- Demonstrated experience in system design, application development, testing, and operational stability for distributed systems and platform services.
- Strong hands-on depth in Python, including building and maintaining APIs/SDKs; ability to lead design and perform high-quality code/design reviews.
- Demonstrated experience with enterprise-authorized AI-assisted development tools (e.g., GitHub Copilot, Claude Code) and experience establishing practical team norms for validation and quality (correctness, performance, security) in day-to-day engineering workflows.
- Experience delivering platforms involving Generative AI, including LLM orchestration patterns and agentic AI frameworks, with production-grade evaluation and monitoring practices.
- Practical experience with AWS, containerized platforms (e.g., EKS/ECS), and Terraform (or equivalent IaC).
- Databricks experience is required, including building and operating data/ML workloads on Databricks (e.g., pipelines, notebooks/jobs, Delta/feature datasets, orchestration/integration with ML workflows).
- Comprehensive knowledge of the Software Development Life Cycle, agile delivery, CI/CD, and modern engineering quality controls.
- Strong communication skills with business-facing partners and technical stakeholders; ability to translate strategy into execution and measurable outcomes.
Preferred qualifications
- Experience building ML platforms integrating data engineering, feature management, and model serving into cohesive developer-friendly workflows.
- Knowledge of AI/ML model integration, context engineering, and MCP-style patterns for tool/function integration.
- Exposure to Snowflake.
- Familiarity with observability/metrics tools (e.g., CloudWatch, Dynatrace, Datadog).
- AWS certifications.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
See All 24 Cloud Data Engineer Jobs in Texas
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Find Cloud Data Engineer JobsCloud Data Engineer Jobs by City in Texas
Where Texas roles are concentrated, by current openings.
Cloud Data Engineer Job Market in Texas
A snapshot from current Texas openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Technology & Software
- Investment & Asset Management
What Texas Employers Look For
The qualifications that appear most often in cloud data engineer jobs across Texas.
- Bachelor's degree in computer science, data engineering, information systems, or a related field
- Hands-on experience with at least one major cloud platform such as AWS, Azure, or Google Cloud
- Proficiency in Python or SQL for building and maintaining data pipelines and ETL workflows
- Experience designing and optimizing data warehouses using tools like Snowflake, Redshift, or BigQuery
- Familiarity with orchestration frameworks such as Apache Airflow, dbt, or similar pipeline management tools
- Strong understanding of data modeling, schema design, and distributed data storage architectures
Cloud Data Engineer Jobs in Texas: Frequently Asked Questions
How do you become a cloud data engineer in Texas?
Cloud data engineering in Texas has no state-issued license or registration requirement, so the path runs through education and vendor credentials. Most Texas employers expect a bachelor's degree in computer science, data engineering, or a related discipline, paired with certifications from AWS, Microsoft Azure, or Google Cloud. Building a portfolio of pipeline and warehousing projects on GitHub and targeting entry-level data engineer or data analyst roles at Texas technology, energy, or financial services firms is the most direct route.
How much do cloud data engineers make in Texas?
Cloud data engineers in Texas earn a median of about $151,370 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $92,890 for the lowest 10% to over $200,390 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire cloud data engineers in Texas?
Employers hiring cloud data engineers in Texas right now include Google, Capital One, and Alvarez & Marsal, based on current listings on Migrate Mate as of November 2026. Texas's concentration of energy conglomerates, major financial institutions, and large enterprise technology firms means sustained, recurring demand rather than hiring tied to a single funding cycle.
Which Texas cities have the most cloud data engineer jobs?
Austin, Houston, and Dallas have the most cloud data engineer openings in Texas. Austin's dense technology sector and corporate headquarters drive most of that city's postings, Dallas benefits from a large concentration of financial services and telecom firms, and Houston's energy industry and sprawling medical center generate consistent demand for engineers who can handle large-scale operational and research data.
Are there remote cloud data engineer jobs in Texas?
Yes, and more than most fields. Cloud data engineering is inherently infrastructure-independent work, making it one of the more remote-compatible technical disciplines. About 60% of cloud data engineer openings tied to Texas are remote or hybrid as of November 2026, reflecting how broadly employers have accepted distributed teams for this role. Pipeline development, warehousing work, and data modeling are the functions most consistently offered on a fully remote basis.
How can I get hired as a cloud data engineer in Texas with little or no experience?
The most realistic entry path is moving laterally from a data analyst or business intelligence analyst role, where SQL skills and familiarity with cloud dashboards translate directly into junior engineering work. Large Texas employers in energy and healthcare, including firms anchored in Houston's medical center and Dallas's financial district, regularly hire associate data engineers willing to train on their internal stack. Earning an AWS Cloud Practitioner or Azure Fundamentals certificate and publishing a capstone pipeline project on GitHub gives candidates a concrete edge when competing for those associate-level openings.
Where can I find and apply to cloud data engineer jobs in Texas?
You can find and apply to cloud data engineer jobs in Texas on Migrate Mate, which lists current Texas openings across industries and experience levels. Find the roles that fit your background and apply directly through the listings on this page.
See All 24 Cloud Data Engineer Jobs in Texas
Find roles in Texas that match your experience and apply in just a few clicks.
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