Support regional planning efforts through advanced land use modeling, data analysis, and forecasting. * Apply strong analytical and programming skills to evaluate demographic, economic, and ...
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The data analyst will analyze, design, create, maintain, update, manage and present Tableau dashboards and stories for the factory management teams. The analyst will also get cross-trained in Cicada ...
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Senior Data Analyst information
See Spring, TX salary details
$48.9K - $55.5K
0% of jobs
$55.5K - $62K
1% of jobs
$62K - $68.5K
3% of jobs
$68.5K - $75K
17% of jobs
$76.3K is the 25th percentile. Wages below this are outliers.
$75K - $81.5K
18% of jobs
The median wage is $85.6K / yr.
$81.5K - $88K
17% of jobs
$88K - $94.5K
18% of jobs
$94.7K is the 75th percentile. Wages above this are outliers.
$94.5K - $101K
17% of jobs
$101K - $107.6K
6% of jobs
$107.6K - $114.1K
1% of jobs
$114.1K - $120.6K
1% of jobs
$48.9K
$88.3K
$120.6K
How much do senior data analyst jobs pay per year?
What is a senior data analyst?
What does a senior data analyst do?
A senior data analyst develops and leads a team of data analysts in business or research projects. Job duties include consulting with other departments and management, conducting research using analytical tools, developing business solutions, and writing reports. Qualifications for a career as a senior data analyst include a bachelor’s degree in statistics, computer science, or a related field; job experience in data analytics; familiarity with data modeling languages such as R and Python; and leadership skills.
What is the difference between Senior Data Analyst vs Data Scientist?
| Aspect | Senior Data Analyst | Data Scientist |
|---|---|---|
| Required Credentials | Bachelor's degree in data-related field; experience in analytics tools | Bachelor's or master's in data science, statistics, or related fields; often more advanced certifications |
| Work Environment | Business intelligence teams, analytics departments | Research teams, data science departments, R&D |
| Employer & Industry Usage | Finance, healthcare, retail, tech companies | Tech firms, startups, research institutions |
| Common Search & Comparison | Often compared for analytical roles with data science | More advanced, predictive modeling focus |
The main difference between a Senior Data Analyst and a Data Scientist lies in their focus and skill set. Senior Data Analysts primarily handle data analysis, reporting, and visualization, while Data Scientists work on predictive modeling, machine learning, and advanced algorithms. Both roles require strong analytical skills, but Data Scientists typically have more technical expertise in programming and statistical modeling.
What are some common challenges senior data analysts face when collaborating with cross-functional teams?
What are the key skills and qualifications needed to thrive as a senior data analyst, and why are they important?
Is a senior data analyst a good job?
How much is a senior data analyst paid?

Full-time
Re-posted 27 days ago
Job description
The Houston-Galveston Area Council is one of the largest regional planning commissions in the country with a diverse service area of 13 counties and more than7million people. We are the pulse of our region addressing issues that cross city limits and county lines every single day.
We make decisions that affect our transportation system, ensure the safety and well-being of our seniors, connect people to jobs, help families recover from natural disasters, preserve water quality for our children, and so much more. We work to make the region a great place to live, work, and thrive.
Job Duties:
Support regional planning efforts through advanced land use modeling, data analysis, and forecasting.
Apply strong analytical and programming skills to evaluate demographic, economic, and geographic trends that inform long-term planning in transportation, land use, environmental sustainability, and economic development.
Apply advanced knowledge in economics, statistics, and data science to develop forecasting models and simulations that guide policy and project development.
Manage and analyze large datasets, build scenario-based models, and communicate findings through technical reports and visualizations, demonstrating excellent written and verbal communication skills.
Develop and maintain land use forecasting models using SAS and Python, incorporating time-series and spatial analysis techniques to support scenario planning.
Conduct simulations to evaluate future land use patterns based on socioeconomic trends and policy drivers.
Collect, clean, and validate demographic, economic, and geographic data from public and private sources.
Ensure transparency and reproducibility by maintaining data dictionaries and process documentation.
Perform quality assurance checks to ensure accuracy of datasets and models, produce technical outputs such as reports and dashboards, and support internal and external stakeholders in interpreting model results.
Respond to data requests related to population, employment, and land use from local governments, research institutions, consultants, and the public, and serve as the primary contact for external data inquiries.
Present technical concepts clearly to non-technical audience.
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Key Qualifications
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Minimum Education Requirement: Bachelor's degree in Urban Planning, Economics, Mathematics, Statistics, Data Science, or a related field.
Minimum Experience Requirement: 36 months of experience developing forecasting models, conducting statistical and spatial analysis, and managing complex datasets, including experience applying proficiency in SAS, and experience in land use modeling, simulation, or scenario analysis.Â