GeoSpatial developer role focuses on designing and implementing robust Java based spatial data solutions for enterprise environments combining database and SQL expertise with Spark driven processing to deliver accurate location intelligence in a hybrid work model while collaborating across teams to improve products and services that rely on geospatial insights for business and societal impact.
Responsibilities
- Design and develop scalable geospatial applications using Java that process complex spatial datasets and deliver reliable location intelligence services for internal platforms and external clients
- Implement efficient database models for spatial data while optimizing SQL queries to ensure high performance retrieval analysis and reporting across large volumes of geospatial information
- Build and maintain Spark based data processing pipelines that transform enrich and aggregate spatial and non spatial data to support advanced analytics and near real time decision making
- Integrate geospatial services with existing enterprise systems by developing clean reusable Java components that support secure data exchange and consistent spatial data standards
- Collaborate with data engineers and analysts to translate business requirements into technical solutions that leverage spatial data and Spark processing for measurable operational and societal outcomes
- Conduct detailed performance tuning for Java services Spark jobs and database queries to reduce latency improve throughput and ensure stability in day shift production environments
- Develop and maintain automated tests for Java code database interactions and data processing workflows to safeguard data quality spatial accuracy and system reliability over time
- Investigate and resolve complex production issues related to spatial data quality Spark jobs or database performance by performing structured root cause analysis and implementing durable fixes
- Document application architectures data models and geospatial processing logic in clear technical artifacts that help teams maintain systems and onboard new colleagues efficiently in a hybrid work setup
- Collaborate with product and domain stakeholders to evaluate new geo data sources coordinate schema changes and refine spatial algorithms that enhance customer experience and support sustainable solutions
- Contribute to continuous improvement initiatives by proposing enhancements to geospatial data pipelines Java frameworks and database structures that reduce operational overhead and improve analytic capabilities
- Apply secure coding practices and data protection guidelines to all Java and database work ensuring responsible handling of geospatial data in alignment with company values and regulatory expectations
- Mentor peers by sharing best practices in geospatial development Java performance optimization and Spark data processing thereby strengthening team capability and enabling higher impact delivery
Qualifications
- Bring strong hands on experience with core Java programming focusing on modular design clean coding practices and familiarity with common enterprise frameworks relevant to geospatial services
- Demonstrate advanced proficiency in relational database design SQL tuning indexing strategies and handling large structured datasets to support spatial queries and analytical reporting needs
- Exhibit practical experience with Spark based data processing including batch and microbatch workloads for transforming and aggregating large scale spatial and non spatial data sources
- Show solid understanding of geospatial concepts such as coordinate systems spatial indexing and geometry operations along with experience using spatial extensions or libraries in databases or Java
- Display capability to work effectively in a hybrid work model coordinating with cross functional teams through clear communication documentation and proactive alignment on technical deliverables
- Possess strong problem solving skills in diagnosing performance bottlenecks and data quality issues across Java services Spark jobs and database layers using systematic investigative approaches
- Prefer experience with additional big data tools or cloud data platforms that complement Spark and support enterprise grade deployment of geospatial analytics solutions
- Appreciate familiarity with modern version control practices continuous integration pipelines and automated testing frameworks to maintain high reliability in evolving geospatial applications
- Value exposure to projects where geospatial insights contributed to tangible business or societal benefits demonstrating understanding of how spatial analytics drives better decisions and outcomes