Principal Software Engineer, Quality Management

As a Principal Software Engineer focusing on Quality Management at Precisely, your main responsibilities include migrating geospatial data processes onto our distributed computing platform, developing validation frameworks for geospatial accuracy, and collaborating with experts to modernize these processes. You'll also be involved in automating quality checks and building dashboards that facilitate real-time data monitoring.

To excel in the Principal Software Engineer role at Precisely, candidates should possess at least a Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. Additionally, having a minimum of 8 years of software testing experience, with at least 5 years focusing on geospatial technologies and frameworks, is essential.

At Precisely, ensuring data quality involves defining test strategies for geocoding datasets to guarantee precision and integrity. The Principal Software Engineer will be tasked with developing validation frameworks and identifying discrepancies that can impact data quality, recommending solutions to enhance these processes.

Candidates interested in the Principal Software Engineer position at Precisely should be proficient in programming languages like Java, Python, and Groovy. Additionally, expertise in crafting optimized SQL queries and experience with scripting automation in Gradle and Maven will significantly benefit your application.

Precisely boasts a 'work from anywhere' culture that promotes diversity and inclusivity. As a Principal Software Engineer, you'll be part of a vibrant team that values openness, individuality, and collaboration, and works on exciting geospatial solutions in a globally distributed environment.

When migrating geospatial datasets, I would start by conducting a thorough analysis of the current processes and identifying potential challenges. Next, I'd create a detailed migration plan that includes data mapping and validation requirements, followed by iterative testing to ensure data integrity throughout the migration process.

I have developed validation frameworks by leveraging industry best practices to ensure data accuracy and completeness. This usually involves defining validation rules, automating checks using scripts, and implementing a systematic approach to identify and resolve data discrepancies.

To ensure data accuracy in geospatial datasets, I recommend implementing robust validation frameworks, performing regular audits, and using automated tools to monitor data flows in real-time. Continuous collaboration with domain experts also enhances accuracy through shared insights and knowledge.

In my previous role, I encountered significant inconsistencies in geospatial data due to integration issues with an external API. I managed to resolve this by conducting root cause analysis, implementing a reporting mechanism to track discrepancies, and enhancing the data pipeline to better handle such inconsistencies in the future.

In an Agile environment, I prioritize tasks using a combination of business value and technical complexity. I engage in regular stand-ups to align with the team on goals, and I keep an adaptable mindset to respond to changing priorities as project needs evolve.

I have worked with various tools, including GIS software and geocoding APIs, as well as data processing technologies like Spark and AWS services. These tools have enabled me to effectively manage large-scale geospatial datasets and automate data quality checks.

I ensure effective collaboration by establishing clear communication channels and regular check-ins to discuss project updates, data quality goals, and feedback on deliverables. By fostering an open environment, I make sure that everyone is aligned and encouraged to share their expertise.

Documentation is critical in my workflow as it ensures that software functionality and processes are clearly communicated among team members. It serves as a reference point for future enhancements and provides clarity on data quality standards and validation techniques.

I have extensive experience with ETL processes, primarily by designing and optimizing data pipelines for extracting, transforming, and loading data. This involves using various tools to streamline data integration and ensure that data adheres to quality metrics throughout the ETL process.

I stay updated on emerging technologies by attending industry conferences, participating in webinars, and engaging with online communities focused on geospatial data. I also regularly read publications and research papers to understand innovative approaches to geospatial analysis and data management.