Senior Software Engineer, Data Platform

Senior Software Engineer, Data Platform @ Medecision | Jobright.ai

Senior Software Engineer, Data Platform jobs in United States

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Medecision · 3 weeks ago

Senior Software Engineer, Data Platform

United States

Full-time

Remote

Senior Level

$130K/yr - $155K/yr

5+ years exp

Medecision is seeking a Senior Software Engineer for their Data Platform team to design, build, and evolve their cloud-native data backbone. The role involves developing data ingestion pipelines and leveraging AI to enhance software engineering workflows while ensuring compliance with healthcare regulations.

HealthcareInformation TechnologySoftwareHealth Care

No H1B

Responsibilities

Design, develop, and maintain production-grade Python and Java data services and pipelines deployed on Google Cloud Platform, following established architectural conventions, coding standards, and data platform patterns

Build and evolve Google Cloud Dataflow batch and streaming pipelines for data ingestion, standardization, curation, and analytics load. Handling deduplication, validation, member-reference integrity, and incremental/full-reload modes

Implement and maintain event-driven data workflows using Google Cloud Pub/Sub, including file-complete notification topics, FHIR ingest topics, and Pub/Sub ? BigQuery export subscriptions

Design and manage BigQuery datasets, table schemas, partitioning strategies (range-bucket by member partition), clustering, and reporting views — across standard analytics, custom analytics, and curated storage layers

Build and maintain Cloud Composer (Apache Airflow) DAGs for workflow orchestration — including file ingestion DAGs, test execution DAGs, and third-party processing DAGs (e.g., MEG)

Develop and maintain Cloud Run microservices (e.g., Ingestion Event Service, Custom Dataset Management Service) and Cloud Functions (inbound GCS bucket triggers)

Participate in design and code reviews; mentor junior engineers and contribute to shared coding standards, patterns, and team knowledge base

Collaborate with on-shore and off-shore teams, architects, and tech leads to ensure on-time delivery and best engineering practices

Contribute to CI/CD pipeline improvements using GitLab CI/CD, including build, test, containerization (Docker), and deployment automation to GCP environments

Engage proactively in the triage and resolution of escalated production issues — diagnosing failures, investigating root causes, and driving durable fixes with a sense of urgency, clear communication to stakeholders, and a commitment to preventing recurrence

Follow and comply with all security policies and procedures established by the organization, including adherence to HIPAA and HITRUST regulations

Own and evolve the end-to-end data ingestion pipeline

from SFTP/GCS file receipt through ingestion, standardization, curation, and load to BigQuery analytics datasets

Design and implement custom dataset ingestion capabilities — including dynamic schema mapping, configuration-driven pipeline execution, and automatic BigQuery table/view provisioning on dataset activation

Maintain and improve the Ingestion Event Service (IES). The platform's central tracking service for data ingestion progress — including Pub/Sub async processing and Firestore document management

Implement FHIR R4 data ingestion workflows via Pub/Sub and GCP Healthcare Datasets, including XSD/schema validation and HL7 resource mapping

Support population matching data flows, ensuring custom dataset filters integrate correctly with population builder services and BigQuery analytics queries

Build and maintain reporting dataset views (BI-compatible BigQuery views) for tenant data exposure

Integrate with third-party clinical analytics engines via GCE VM instance templates, Airflow DAGs, and GCS-based data exchange

Ensure all data services meet HIPAA requirements

PHI handling, tenant data isolation, audit logging, and data classification

Demonstrate a solid understanding of AI concepts, capabilities, and limitations as they apply to software engineering workflows, including code generation, test scaffolding, and documentation

Use Claude Code as a primary productivity tool for code drafting, refactoring, test generation, and technical documentation — applying it with judgment, rigor, and accountability

Leverage AI-assisted workflows to accelerate implementation, surface edge cases, generate structured artifacts, and conduct at-scale analysis of service dependencies and API contracts

Contribute to building and exposing MCP-wrapped APIs that enable AI agents to safely interact with platform services

Maintain strict HIPAA discipline in all AI-assisted work

no real PHI in prompts or AI-generated artifacts; adhere to managed-settings policies and complete mandatory HIPAA + AI training

Contribute to the team's shared AI knowledge base. Validated prompts, skills, and workflows — and participate in the AI Champions community of practice

Qualification

PythonJavaGoogle Cloud PlatformBigQueryCloud StorageCloud Pub/SubCloud DataflowApache BeamCloud ComposerApache AirflowCloud RunCloud FunctionsFirestoreCloud SQLPostgreSQLSecret ManagerData pipeline developmentBatch and streaming data pipelinesFile-based ingestionEvent-driven processingDeduplicationValidationIncremental loadFull-reload patternsBigQuery data modelingPartitioned tablesClustered tablesSQL query optimizationContainerizationDocker

Required

Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or equivalent practical experience

5+ years of data engineering or backend software engineering experience building production data pipelines and platform services

Proven hands-on experience with Google Cloud Platform data services: BigQuery (schema design, partitioning, clustering, query optimization), Cloud Storage, Cloud Pub/Sub, Cloud Dataflow (Apache Beam), Cloud Composer (Airflow), Cloud Run, Cloud Functions, Firestore, Cloud SQL (PostgreSQL), and Secret Manager

Strong proficiency in Java for data pipeline development

Proficiency in Python for Airflow DAG authoring, and automation scripting

Experience designing and implementing batch and streaming data pipelines — including file-based ingestion, event-driven processing, deduplication, validation, incremental load, and full-reload patterns

Proficiency with BigQuery data modeling

partitioned and clustered tables, dataset organization (standardized, curated, analytics, custom analytics, reporting layers), and SQL query optimization

Experience with Apache Airflow / Cloud Composer — authoring, deploying, and maintaining production DAGs with parameterized configurations and robust error handling

Experience with containerization (Docker) and deploying services to cloud-native environments

Proficiency with GitLab CI/CD for pipeline automation and multi-environment deployment

Excellent communication skills — able to articulate technical decisions, participate in design reviews, and collaborate effectively with cross-functional teams

Familiarity with Datadog for service monitoring, alerting, and observability in a cloud-native data platform

Familiarity with Sisense or equivalent BI/reporting platforms and BigQuery view-based reporting patterns

Solid understanding of AI concepts, capabilities, and limitations as they apply to software engineering and product delivery workflows

Hands-on experience with Claude Code or equivalent AI-assisted tools. Used as a primary productivity tool for code generation, refactoring, test scaffolding, and documentation, not just experimentally

Ability to evaluate AI-generated code critically

identifying hallucinations, logic errors, security gaps, and missing edge cases before they reach production

Practical understanding of MCP (Model Context Protocol) or strong willingness to learn — for building tool wrappers that expose platform APIs to AI agents safely and with appropriate guardrails

Commitment to responsible AI use

applying AI with judgment, rigor, and personal accountability. Consistent with the principle that humans own decisions, agents own toil

HIPAA discipline in AI-assisted work

understanding of PHI boundaries in AI workflows and commitment to managed-settings policies and mandatory HIPAA + AI training

Openness to contributing to and learning from a shared AI knowledge base. Validated prompts, skills, and workflows — and active participation in the AI Champions community of practice

Preferred

Knowledge of HIPAA and experience working in HIPAA-regulated product environments, including PHI handling, data classification, and audit requirements

Hands-on experience with HAPI FHIR R4 and healthcare interoperability standards (HL7, FHIR resource mapping, validation workflows)

Understanding of multi-tenant SaaS architecture patterns — tenant context propagation, per-tenant feature flags, and data isolation

Company

Medecision

Cloud-based care management software for health plans and providers.

Founded in 1988

Wayne, Pennsylvania, USA

201-500 employees

http://www.medecision.com

Funding

Current Stage

Public Company

Total Funding

$30M

2008-06-18Acquired

2006-12-13IPO

2003-03-07Series C

Leadership Team

Kenneth Young

Chief Executive Officer

Recent News

Morningstar.comManaged Care and Health Plan Clients Rate Medecision #1 for Care Management, Case Management and Medical Management Platforms in Black Book's 2026 Payer IT Survey

2026-06-11

HealthCareIT NewsBest in KLAS 2026 sees positive disruption, 'tangible' tech improvements

2026-02-06

PR NewswireMedecision Announces Strategic Partnership with Sendero Health Plans to Enhance Community-Based Health Outcomes in Central Texas

2025-12-18

Company data provided by crunchbase