Senior Data Engineer (Healthcare Data)

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

The requirements below describe our ideal candidate. If you don’t meet every single one but believe you would be a strong fit for this role, we still encourage you to apply – we will consider all candidates whose experience matches most of the requirements.

• 5+ years of hands-on experience in data engineering, with expertise in distributed data processing and big data frameworks (e.g., Apache Spark, Apache Iceberg, Trino, dbt, Airflow, Dagster)

• Advanced SQL skills, including untangling complex, undocumented PostgreSQL view logic and writing production-quality SQL in Trino or similar engines (MERGE and upsert patterns)

• Hands-on experience with MPP databases such as Amazon Redshift, Snowflake, or Teradata

• Strong ETL/ELT experience with incremental loads, idempotency, upserts, late-arriving and out-of-order data, and entity dependencies

• Advanced programming skills in Python or Scala, including parsing XML/JSON and working with graph or tree structures

• Strong experience in Spark performance tuning and optimizing large-scale data workflows

• Experience with real-time streaming technologies such as Apache Flink, Spark Streaming, or Kafka

• Experience building and operating pipelines in Dagster (preferred) or Airflow, including scheduling, dependencies, and backfills

• Experience reconciling outputs across systems using aggregate checks, field-level match rates, golden test fixtures, and data mapping and testing frameworks

• Hands-on CI/CD experience for data pipelines: Git-based workflows, pull request reviews, GitHub Actions, Jenkins, or Terraform, and automated testing of SQL and dbt models

• Familiarity with AWS (EC2, S3, RDS PostgreSQL, DynamoDB, cross-account IAM role assumption) and containerized infrastructure (EKS, Docker, Kubernetes)

• Experience with Apache NiFi, including reading and debugging complex flows, process groups, parameter contexts, and ExecuteScript processors

• Ability to read and understand Java/Groovy source code of custom NiFi processors

• Hands-on experience with AI coding tools such as Cursor, Claude Code, or Copilot, including spotting subtle errors in AI-generated code

• Experience handling PHI under HIPAA controls or in equivalent regulated-data environments

• Exposure to machine learning/deep learning (PyTorch preferred, TensorFlow or Keras) and MLOps tools for model deployment and lifecycle management

• A self-starter mindset with strong problem-solving skills, comfortable navigating ambiguous, undocumented systems in a fast-paced, agile environment

Nice to have

• Experience with healthcare data standards (HL7, FHIR), including FHIR resources, validation, and mapping tools

• Familiarity with payer data (claims, eligibility, members, and coverage)

• Familiarity with reporting and visualization tools such as Superset, Grafana, or Tableau

• Understanding of data security, compliance, and governance frameworks (e.g., GDPR, SOC 2)

What we offer:

• Competitive salary and benefits package.

• 23 days of paid leave.

• Opportunities for professional growth and development.

• A collaborative work environment with talented and dedicated colleagues.

If you're passionate about building reliable data systems that make a real impact in healthcare, we'd be excited to have you on board.