Technical skills
About this role
Salt Square is a growing outsourcing company providing high-quality software development services to clients across a wide range of industries. Our team is composed of skilled and dedicated professionals delivering innovative solutions that meet and exceed client expectations.
Our mission is to build reliable, scalable, and future-ready digital solutions tailored to each client’s unique business needs. We foster a team-first culture where collaboration, continuous learning, and technical excellence are core values. From supporting startups in launching their first products to helping global enterprises scale their operations, we strive to deliver outstanding value on every project.
As a Data Engineer at Salt Square, you will build and maintain scalable healthcare data pipelines, transform legacy claims and clinical data into the FHIR® (Fast Healthcare Interoperability Resources) standard, and support population health analytics and regulatory compliance for payers and providers.
We are looking for an experienced Senior Data Engineer to design, implement, and optimize scalable data solutions. You will work with modern data technologies, distributed systems, and cloud platforms to build efficient, high-performance data pipelines that support analytics, AI/ML, and business intelligence initiatives.
Typical Responsibilities
- Reverse-engineer legacy transformation logic across NiFi flows, PostgreSQL views and materialized views, and custom FHIR mappings
- Develop Python tooling to parse NiFi flow definitions (XML/JSON, NiFi 1.12 and 1.19) and flatten nested process groups into a readable, ordered structure
- Design, develop, and implement robust ETL/ELT pipelines for large-scale data ingestion, transformation, and storage, including rebuilding data feeds with dbt models and Trino MERGE transformations
- Use AI coding agents to accelerate development while rigorously reviewing the generated code
- Ensure data quality, integrity, and governance through validation techniques, data monitoring, and automated testing
- Design and execute parity validation between legacy and new outputs, respecting restrictions on moving row-level data outside protected environments
- Automate deployments and testing using CI/CD pipelines with Git, Terraform, GitHub Actions, or Jenkins
- Develop alerting, monitoring, and observability frameworks for data pipelines to ensure reliability and proactive issue resolution
- Design and build custom data tools and abstractions to support analytics, machine learning, and real-time data processing
- Document legacy bugs, ambiguities, and obsolete code paths without unnecessarily blocking migration progress
- Help establish a repeatable, templated migration workflow that can scale across multiple environments
- Contribute to data architecture and strategy, driving improvements in scalability, performance, and cost optimization
- Stay up to date with emerging technologies and best practices to continuously enhance data engineering capabilities