ZipRecruiter

Log In

1

Data Modernization Jobs in Texas (NOW HIRING)

BravoTech

Data Management Manager

Dallas, TX · On-site

Lead enterprise data modernization programs, including migration from legacy platforms to cloud-based Data Lake and Lakehouse solutions. Implement best practices for data ingestion, transformation ...

Tata Consultancy Service Limited

Data Architect

Dallas, TX · On-site

$120K - $140K/yr

... data modernization initiatives, define target-state architecture, and establish scalable data platforms supporting analytics, AI, governance, and business intelligence. The ideal candidate will ...

Tror AI for everyone

Google Cloud Data Architect

Dallas, TX · On-site

$63 - $81/hr

Google Cloud Data Architect - IAM Data Modernization Job Location: Dallas, TX (4 days onsite) Job Type: Contract Note: Need 13- 15Years of experience resumes. Required Qualifications * 5+ years ...

Neos Consulting

Data Architect

Austin, TX · On-site

$63.25 - $81.25/hr

Data Architect - Enterprise Data Modernization (D2I Initiative) Environment: Higher Education | Enterprise Data | Hybrid Mainframe + Cloud Position Overview The Data Architect will support UT Austin ...

Tata Consultancy Service Limited

Data Engineer

Dallas, TX · On-site

$105K - $120K/yr

Roles & Responsibilities We are seeking a highly skilled Data Engineer with strong expertise in Snowflake, dbt, SQL, Python, and AWS to support enterprise data modernization initiatives. The ideal ...

NAVA Software Solutions

Cloud Data Manager

Houston, TX · On-site

As our organization is implementing a data modernization program along with data and digital strategies, the Cloud Data Manager will play a key role in providing input and influence in our journey ...

next page

Showing results 1-20

All JobsData Modernization JobsData Modernization Jobs in Texas

Data Modernization information

What jobs pay $500,000 a year in the US?

In the field of data modernization, senior roles such as Chief Data Officer, Data Science Director, or Vice President of Data often have salaries reaching or exceeding $500,000 annually, especially in large organizations. These positions typically require extensive experience, advanced skills in data architecture, analytics, and leadership, and may include performance bonuses and stock options.

What is the difference between Data Modernization vs Data Analyst?

AspectData ModernizationData Analyst
Primary FocusUpgrading and transforming data systems and infrastructureAnalyzing data to generate insights and reports
Skills RequiredData architecture, cloud platforms, database managementStatistical analysis, data visualization, SQL
Work EnvironmentIT departments, data engineering teamsBusiness units, analytics teams
CertificationsCloud certifications, data management certificationsData analysis, visualization certifications

Data Modernization involves upgrading data systems and infrastructure to improve efficiency and scalability, often requiring technical expertise in data architecture and cloud platforms. In contrast, Data Analysts focus on interpreting data, creating reports, and providing insights to support business decisions. While both roles work with data, their core responsibilities and skill sets differ significantly.

What jobs make $1,000,000 a year?

In the field of data modernization, high-paying roles such as Chief Data Officer, Data Science Director, or Chief Technology Officer can earn over $1 million annually, especially in large organizations or tech companies. These positions typically require extensive experience, advanced skills in data management, leadership, and often involve overseeing large teams and strategic initiatives.

What is data modernization?

Data modernization is the process of updating and transforming legacy data systems and infrastructure to more current, scalable, and efficient technologies. It often involves migrating data to cloud platforms, implementing new data management tools, and adopting modern analytics and automation techniques to improve data accessibility and decision-making.

What are some common challenges faced by professionals working in Data Modernization projects?

Professionals in Data Modernization often encounter challenges such as integrating legacy systems with modern cloud-based solutions, ensuring data quality during migration, and managing data security and compliance. Additionally, they may need to collaborate closely with cross-functional teams to align business goals with technical requirements. Adaptability and strong communication skills are important, as priorities can shift rapidly in response to evolving business needs and technology updates.

What are the key skills and qualifications needed to thrive in Data Modernization, and why are they important?

To thrive in Data Modernization, you need strong expertise in data architecture, cloud platforms, and data migration, often supported by a degree in computer science or information systems. Familiarity with tools like Azure, AWS, Snowflake, ETL frameworks, and certifications such as AWS Certified Data Analytics or Microsoft Azure Data Engineer are commonly required. Excellent problem-solving, project management, and communication skills help professionals effectively lead transformation initiatives and collaborate with stakeholders. These skills are crucial for ensuring seamless migration, maximizing data value, and driving innovation within organizations.

Which 3 jobs will survive AI?

Data modernization professionals, data analysts, and database administrators are likely to continue thriving as AI automates routine tasks but still requires human oversight, interpretation, and strategic decision-making. These roles involve managing complex data systems, ensuring data quality, and applying domain expertise that AI cannot fully replicate. Skills in data governance, programming, and understanding AI tools will enhance job security in this field.
What are popular job titles related to Data Modernization jobs in Texas? For Data Modernization jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Data Modernization jobs in Texas look for? The top searched job categories for Data Modernization jobs in Texas are:
What cities in Texas are hiring for Data Modernization jobs? Cities in Texas with the most Data Modernization job openings:
Data Modernization jobs near you
Infographic showing various Data Modernization job openings in Texas as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Data Architect - OCP (OpenShift) / IAM Data Modernization

Carman Solutions Group

Dallas, TX • On-site

$63 - $81/hr

Contractor

Re-posted 10 days ago

Job description

Job Title: Data Architect – OCP (OpenShift) / IAM Data Modernization
Location: Dallas, TX or Charlotte, NC (100% Onsite)
Duration: 6-12 Months
Objective:
• Migration of an on-premises SQL data warehouse to a modern enterprise Data Lake platform, enabling analytics and GenAI use cases.
• The platform leverages PySpark-based processing, CI/CD pipelines, and containerized deployments on OpenShift (OCP), with GCP as a preferred cloud platform, to deliver scalable, secure, and high-performance data solutions.
About Program/Project:
• The IAM Data Modernization program focuses on transforming legacy data platforms into a scalable and cloud-compatible architecture.
Key Highlights:
• Integration Scope: 30+ source systems with multiple downstream integrations.
• Capabilities: Metrics, reporting, advanced analytics, and GenAI use cases (NL querying, summarisation, cross-domain insights).
Benefits:
• Scalable and resilient data platform.
• High-performance semantic and analytics layer.
• Single source of truth for enterprise-wide reporting and analytics.
Role Summary:
• We are looking for a Data Architect with strong expertise in OpenShift (OCP), PySpark, and CI/CD pipelines to design and govern scalable data platforms.
• The role requires defining end-to-end data architecture, containerised deployment patterns, orchestration strategies (Airflow/Autosys), and platform standards, along with hands-on involvement in implementation.
Key Responsibilities:
Data Architecture & Platform Design:
• Define enterprise data architecture for IAM data lake and analytics platform.
• Design scalable, modular, and containerised data pipeline architectures on OCP.
• Establish data models, schema governance, and data lifecycle strategies.
• Define best practices for data partitioning, performance optimisation, and cost efficiency.
OpenShift (OCP) & Platform Engineering:
• Architect and govern containerised data workloads on OpenShift (OCP).
• Define standards for deployment, scaling, and workload isolation.
• Collaborate with DevOps teams for platform engineering and infrastructure alignment.
Big Data & Processing (PySpark Focus):
• Define architecture for PySpark-based batch and near real-time processing pipelines.
• Provide guidance on distributed processing design, optimisation, and performance tuning.
• Establish reusable frameworks for ETL/ELT processing.
Data Ingestion & Orchestration:
• Architect data ingestion frameworks (batch, streaming, CDC).
• Define orchestration strategies using Airflow/Autosys.
• Implement standards for retry, backfills, dependency management, and error handling.
DevOps/CI-CD:
• Define and oversee CI/CD strategy for data and platform deployments.
• Enable automation of build, test, and deployment processes.
• Ensure integration of CI/CD pipelines with OCP-based environments.
Cloud & Data Platforms (Preferred):
• Provide architecture guidance for GCP-based data platforms (preferred, not mandatory).
• Define integration patterns for cloud-native and on-premise hybrid environments.
• Guide teams on cloud migration strategies and modern data platform adoption.
Data Governance, Quality & Observability:
• Data quality, validation, and lineage.
• Metadata management and cataloguing.
• Establish monitoring, logging, alerting, and SLOs for platform reliability.
• Ensure compliance with data security and audit requirements.
• Stakeholder Collaboration.
• Work closely with client architects, IAM teams, and business stakeholders.
• Translate business requirements into scalable technical architecture.
• Provide architectural guidance and mentorship to engineering teams.
Required Skills - Core Skills (Must Have):
• OpenShift (OCP)/Kubernetes-based platforms.
• PySpark/Spark ecosystem.
• CI/CD implementation for data platforms.
• Airflow/Autosys orchestration tools
Solid Understanding Of:
• Data lake architectures (layered models).
• ETL/ELT design patterns.
• Distributed data processing concepts.
Data Engineering & Storage:
• Data formats: Parquet, ORC, Avro.
• Partitioning and performance tuning.
• Large-scale data modelling for analytics.
• Cloud (Preferred – Not Mandatory).
• Experience with Google Cloud Platform (GCP) (preferred).
• Exposure to services like BigQuery, Dataproc, Dataflow, GCS is a plus.
Observability & Reliability:
• Monitoring, logging, alerting frameworks.
• Dashboards, SLOs, and operational runbooks.
Good To Have:
• Experience with IAM domain/cybersecurity data.
• Understanding of data security and access control frameworks.
• Exposure to GenAI-enabled data platforms.
• Experience in Agile delivery and team leadership.
Experience:
• 10–14+ years in Data Architecture/Data Engineering.
• Strong experience in OCP, PySpark, CI/CD, and orchestration frameworks.
• Prior experience in data modernization/migration programs.
Education:
• Bachelor’s/Master’s in Computer Science, Information Systems, or equivalent.
Certifications (Preferred):
• OpenShift/Kubernetes certifications.
• GCP certifications (preferred, not mandatory).

Regards,

Himanshu Rawat

himanshu@carmansg.com