Build and maintain reliable analytical datasets from raw, messy, and large-scale sources. Collect and automate data ingestion (web, APIs, public datasets), clean/validate/deduplicate data, create standardized data models, and perform profiling, enrichment, and geospatial analysis to produce business-ready insights. Define repeatable workflows, SOPs, and collaborate on data pipelines and automation.
Senior Data Analyst – Data Operations & Geospatial Intelligence
Role Overview
Data Collection & Processing
Requirements
Role Overview
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.
Data Collection & Processing
- 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
Requirements
Data Analysis, Data clenaing, Data operation, Powerbi, looker, studio
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