Senior Data Analyst

We are looking for a Senior Data Analyst who can transform raw, complex, and unstructured datasets into clean, structured, business-ready information. This role requires strong experience in data cleansing, data validation, data modeling, and analytical workflows using Python and Pandas.

The ideal candidate should have experience working with large-scale datasets, public/government data sources, web-based data collection, and location-based/geospatial information. This is a hands-on data operations role focused on building reliable datasets that support business decision-making.

• Collect, extract, and analyze data from multiple sources including websites, portals, APIs, public datasets, and semi-structured sources.
  
• Work with raw and messy datasets and convert them into standardized, usable formats.
  
• Perform data extraction, transformation, cleaning, and enrichment activities.
  
• Support web scraping and automated data collection workflows.
  

• Clean, normalize, deduplicate, and validate large datasets.
  
• Identify data quality issues and implement validation rules and quality checks.
  
• Create standardized data models, field definitions, and data structures.
  
• Ensure datasets are accurate, complete, and ready for business use.
  

• Use Python and Pandas for data manipulation, transformation, and analysis.
  
• Build analytical datasets from raw information sources.
  
• Perform data profiling, classification, enrichment, and trend analysis.
  
• Translate complex datasets into meaningful business insights.
  

• Work with location-based datasets, mapping information, and geospatial data.
  
• Analyze geographic attributes such as addresses, coordinates, locations, and spatial relationships.
  
• Experience with GIS tools, Google Maps platforms, mapping APIs, or location intelligence solutions is preferred.
  

• Define repeatable data workflows, documentation, and SOPs.
  
• Collaborate with technical teams on automation, scraping, and data pipelines.
  
• Review and improve data quality processes.
  

• 5–8 years of experience in Data Analytics, Data Operations, Data Quality, Business Intelligence, or related roles.
  
• Strong experience handling raw, incomplete, and inconsistent datasets.
  
• Advanced Python skills with strong hands-on experience using Pandas.
  
• Experience with SQL for querying, validating, and analyzing data.
  
• Strong understanding of data cleaning, transformation, and data modeling.
  
• Experience building data validation and quality-check processes.
  
• Strong analytical and problem-solving skills.
  

• Experience working with government/public sector datasets or government projects.
  
• Experience with web scraping, data extraction, OCR, or document parsing.
  
• Experience with geospatial data, GIS, mapping platforms, Google Maps, or location intelligence.
  
• Experience working with large-scale datasets.
  
• Knowledge of APIs and automated data collection workflows.
  
• Experience with Power BI/Tableau or other visualization tools is a plus.
  

Candidates from the following backgrounds are preferred:

• Government data projects
  
• Public sector analytics
  
• GIS / Geospatial analytics
  
• Location intelligence
  
• Data quality and data operations teams
  
• Market intelligence / research analytics
  
• Large-scale data processing environments