SoundThinking is seeking an experienced Data Conversion/ ETL Engineer to join our team in converting data from third-party legacy applications to our cloud-based SaaS solutions.
This role requires a strong background in Python (primary language), Java, and SQL/PostgreSQL, with an emphasis on data migration, conversion, and scalable data architectures. Experience in SaaS environments is essential, and familiarity with law enforcement data is highly preferred.
The ideal candidate will work closely with cross-functional teams, including product, engineering, and customer success, to ensure the seamless migration of data from legacy systems to modern cloud-based solutions. The role will involve designing and implementing data architectures, optimizing ETL processes, and ensuring the quality and integrity of data.
Essential Duties/ Responsibilities
- Lead end-to-end data conversion processes, including planning, execution, and validation.
- Design, develop, and maintain modules / classes primarily in Python and Java to facilitate data migration and transformation.
- Develop and optimize ETL (Extract, Transform, Load) processes, ensuring data is efficiently migrated and transformed across systems.
- Utilize Python for data extraction, transformation, and loading tasks, ensuring reusable and efficient code that optimizes ETL workflows.
- Familiarity with Object Relational Mapping, build tools, and validation libraries (hatch, pydantic, sqlalchemy)
- Experience with TDD (Test Driven Development)
- Analyze and evaluate database schemas in various relational database systems, including MS SQL Server, PostgreSQL, and Oracle DB, to ensure seamless data conversion and migration.
- Ensure data consistency and accuracy through robust data validation and cleansing techniques.
- Develop and maintain data ingestion and processing systems, ensuring smooth data flow from multiple sources.
- Create and implement scalable data architectures to meet business needs and optimize performance.
- Work with clients to extract, transform, and load (ETL) data from legacy systems to cloud-based SaaS solutions.
- Collaborate with cross-functional teams to identify and resolve data-related issues, enhancing overall data management practices.
- Provide technical support and troubleshooting for data migration and architecture-related issues.
- Ensure data integrity, consistency, and security throughout the conversion process, utilizing Python-based validation checks.
- Document procedures and provide technical guidance to other teams as needed, including Python code examples and best practices.
- Communicate progress and address issues with both technical and non-technical stakeholders.
- Stay updated on industry standards and best practices related to data migration, architecture, and conversion.
Minimum Qualifications
- Bachelor’s degree in Computer Science, Information Systems, or a related field (or equivalent work experience).
- 7+ years of experience in data migration, data conversion, or data engineering roles.
- Strong programming experience with Python (primary) and Java.
- Expertise in analyzing database schemas in relational databases, such as SQL, PostgreSQL, and Oracle DB.
- Experience using Github Copilot and/or other AI Coding Assistants.
- Experience with SaaS platforms and cloud technologies.
- Proven experience in developing and optimizing ETL processes.
- Understanding of database structures, data modeling concepts, and data storage solutions (e.g., databases, data lakes).
- Familiarity with law enforcement data systems and regulatory standards (e.g., CJIS compliance) is highly preferred.
- Strong problem-solving and analytical skills, with an ability to troubleshoot and debug Python scripts.
- Excellent communication and collaboration abilities, with the ability to work across departments and communicate with both technical and non-technical stakeholders.
- Previous experience in law enforcement, government, or public sector data projects preferred.
- Must be able to pass CJIS clearance.
- Experience with Generative AI is highly desired
