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Data Analytics Python Jobs in Texas (NOW HIRING)

TrinityRail

Data Analytics Intern

Dallas, TX · On-site

Trinity Industry is looking for Data Analytics Interns for our office in Dallas, TX . This position ... Python, JavaScript, YAML) using an IDE (VS Code, JetBrains, etc.) with AI-assisted coding (GitHub ...

Trinity Industries

Data Analytics Intern

Dallas, TX · On-site

Trinity Industry is looking for Data Analytics Interns for our office in Dallas, TX . This position ... Python, JavaScript, YAML) using an IDE (VS Code, JetBrains, etc.) with AI-assisted coding (GitHub ...

Citigroup, Inc.

Data Analytics Senior Analyst

Irving, TX · On-site

$141K - $142K/yr

Tableau, Excel, PowerBI; and Pyspark, Hadoop, Hive, python, scripting, MongoDB. Applicants submit ... Data Analytics Job Family Group: Job Family: Time Type: Full time Primary Location: Irving Texas ...

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Data Analytics Python information

Is 40 too late for data science?

Data Analytics Python roles often value skills and experience over age, and many professionals transition into data science later in their careers. Learning relevant tools like Python, SQL, and machine learning can help you enter the field regardless of age, and continuous education or certifications can improve your prospects.

What are some typical challenges faced when working as a Data Analytics Python professional, and how can they be addressed?

Data Analytics Python professionals often encounter challenges such as handling large and complex datasets, ensuring data quality, and optimizing code for performance. Collaborating with cross-functional teams to understand business requirements and communicating insights clearly can also be demanding. To address these challenges, it's important to stay updated with best practices in data cleaning, leverage efficient libraries like pandas and NumPy, and engage in regular communication with stakeholders to align on project goals. Additionally, participating in code reviews and continuous learning can help maintain high standards and drive professional growth.

What are Data Analytics Python professionals?

Data Analytics Python professionals are specialists who use the Python programming language to analyze, interpret, and visualize data. They apply statistical techniques, build predictive models, and generate insights to help organizations make data-driven decisions. Their work often involves cleaning and preparing data, using libraries like Pandas, NumPy, and Matplotlib, and communicating findings to stakeholders. These professionals are in high demand across industries due to the growing importance of data in business strategy.

What are the key skills and qualifications needed to thrive as a Data Analytics Python professional, and why are they important?

To thrive as a Data Analytics Python professional, you need a strong background in statistics, data interpretation, and proficiency in Python programming, often supported by a degree in computer science, mathematics, or a related field. Familiarity with tools and libraries such as Pandas, NumPy, Matplotlib, Jupyter Notebooks, and possibly certifications in data analytics or Python are highly valuable. Critical thinking, problem-solving ability, and effective communication help translate complex data findings into actionable business insights. These skills are essential for extracting meaningful information from data and driving data-informed decisions in organizations.

Is Python useful for data analysts?

Python is highly useful for data analysts as it offers powerful libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization. It is widely used in the industry for automating tasks, building models, and handling large datasets, making it a valuable skill for the role.

Will AI replace a data analyst?

AI tools can automate routine data processing and basic analysis tasks, but data analysts are essential for interpreting complex data, making strategic decisions, and providing context. The role of a data analyst involves skills like critical thinking, domain knowledge, and communication, which are difficult for AI to fully replicate. Therefore, while AI may change some aspects of the job, it is unlikely to fully replace data analysts in the near future.

What is the salary for Python data analytics?

The salary for a Python data analyst typically ranges from $60,000 to $100,000 annually, depending on experience, location, and industry. Professionals with advanced skills in data visualization, machine learning, and certifications may earn higher salaries. Entry-level positions generally start lower, while senior roles can exceed this range.

What is the difference between Data Analytics Python vs Data Analyst?

AspectData Analytics PythonData Analyst
Required SkillsPython programming, data manipulation, statistical analysisExcel, SQL, basic statistics
CertificationsPython certifications, data analysis coursesNone specific, often data analysis or business certifications
Work EnvironmentData science teams, tech companies, analytics departmentsBusiness units, finance, marketing, consulting firms
Tools & TechnologiesPython, Jupyter, Pandas, NumPy, visualization librariesExcel, SQL, Tableau, Power BI

Data Analytics Python focuses on using Python programming for data analysis, requiring coding skills and advanced statistical knowledge. In contrast, Data Analysts often work with tools like Excel and SQL for data interpretation and reporting. Both roles are essential in data-driven industries but differ in technical depth and toolsets.

What are popular job titles related to Data Analytics Python jobs in Texas? For Data Analytics Python jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Data Analytics Python jobs in Texas look for? The top searched job categories for Data Analytics Python jobs in Texas are:
What cities in Texas are hiring for Data Analytics Python jobs? Cities in Texas with the most Data Analytics Python job openings:
Data Analytics Python jobs near you
Senior Data Analytics Architect

Senior Data Analytics Architect

United IT Solutions

Southlake, TX • On-site

$63.25 - $84.75/hr

Other

Posted 9 days ago

Job description

Mandatory Skills  

·                  15+ years in Data Architecture, Data Analytics, Data governance, and Data Warehousing and architectural thought leadership 

·                  Proven experience with data strategy, roadmap, and end-to-end data landscape 

·                  Strong hands-on experience with Google Cloud Platform, especially Big Query 

·                  Expertise in Informatica IDMC  

·                  Advanced Complex SQL and Python skills  

·                  Experience designing enterprise data warehouse & analytics solutions  

·                  Ability to support and guide development and production systems  

·                  Strong business, communication, and stakeholder management skills 

·                  Google Cloud Platform Certification (Professional Data Engineer / Cloud Architect) 

Good to Have Skills  

·                  Experience with Google Cloud Platform Pub/Sub and Cloud Composer  

·                  Exposure to AI/ML ready data architectures  

·                  BQ Data Modeling and EDC 

·                  Cloud cost optimization experience 

·                  Consulting or large enterprise experience 

·                  Exposure to security and compliance frameworks in enterprise data platforms 

Role Overview

We are seeking an experienced Senior Data Analytics Architect with over 15 years of expertise in designing and delivering enterprise-scale data, analytics, and AI-enabled solutions. This role will lead the architecture, design, and implementation of modern data analytics platforms on Google Cloud Platform (Google Cloud Platform), enabling advanced analytics, data warehousing, and artificial intelligence use cases.

The Senior Data Analytics Architect will work closely with Product teams, development and production support teams, business users, and data consumers to ensure data solutions are scalable, reliable, secure, and aligned with business objectives.

Key ResponsibilitiesData & Analytics Architecture

·        Define and own the enterprise data and analytics architecture, aligning with business strategy and long-term analytics roadmap.

·        Architect end-to-end data warehousing, analytics, and AI-ready data platforms.

·        Design logical, physical, and analytical data models to support reporting, dashboards, advanced analytics, and AI/ML workloads.

·        Establish architecture standards, design patterns, and best practices for analytics and data platforms.

·        Architect data ingestion, streaming, and batch pipelines using Informatica IDMC, Pub/Sub, and Cloud Composer

 Solution Design & Implementation

·        Design and implement cloud-native analytics solutions on Google Cloud Platform, with a strong focus on Big Query.

·        Architect scalable data warehouses and analytics layers optimized for high-performance querying.

·        Design and oversee data ingestion, transformation, and orchestration pipelines using Informatica IDMC.

·        Enable analytics and AI use cases by ensuring data is well-structured, governed, and accessible.

Data Analytics & Artificial Intelligence Enablement

·        Support advanced analytics and AI initiatives by designing data architectures optimized for machine learning and data science workloads.

·        Collaborate with analytics and AI teams to ensure data availability, quality, and performance.

·        Ensure the data platform supports exploratory analytics, predictive modeling, and AI-driven insights.

 Development & Production Support

·        Provide hands-on architectural guidance to development teams throughout the development lifecycle.

·        Review SQL, Python code, and data pipelines for performance, scalability, and reliability.

·        Support production environments, including:

o   Monitoring and troubleshooting data pipeline failures.

o   Performance tuning and optimization

o   Root cause analysis and issue resolution

·        Ensure SLA adherence, data accuracy, and high availability of analytics systems.

 Stakeholder Collaboration

·        Partner with Product teams to translate product and analytics requirements into robust data solutions.

·        Work closely with business users and data consumers to understand reporting, analytics, and insight needs.

·        Communicate complex technical concepts clearly to both technical and non-technical stakeholders.

·       

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