Role Description The Engineer II, Data is primarily responsible for building and operating reliable data integrations and data platform capabilities that move sensitive healthcare data securely and consistently. This includes: - Creating new source ingestion feeds and interfaces - Maintaining and troubleshooting existing interfaces - Improving ingestion, validation, and monitoring - Partnering with stakeholders to deliver analytics, warehousing, and reporting solutions - Contributing to platform engineering practices by automating environments, standardizing deployments, and improving observability and reliability for data services Qualifications - 3 to 5 Years of desired work experience - Bachelor's Degree or Equivalent Experience in Computer Science - 2+ years of coding experience with Microsoft SQL - 1+ years working with big data technologies including but not limited to Databricks, Apache Spark, Python, Microsoft Azure (Data Factory, Dataflows, Azure Functions, Azure Service Bus) - Understanding of engineering fundamentals: testing automation, code reviews, telemetry, iterative delivery, and DevOps - Experience with polyglot storage architectures including relational, columnar, key-value, graph or equivalent - Some experience with BI tools, preferably Power BI - Some experience with cloud-based infrastructure, preferably Microsoft Azure - Experience with infrastructure as code and configuration management (e.g., Terraform) and environment standardization - Familiarity with operational excellence practices such as incident response and runbooks - Understanding of security fundamentals for cloud platforms (identity and access management, secrets management, encryption, and audit logging) - Experience packaging and running services using containers and orchestration (e.g., Docker, Kubernetes) and/or serverless patterns - Experience with CI/CD, version control, and release automation for data pipelines and platform components (e.g., Azure DevOps/GitHub Actions) - Very strong attention to detail - Must be inquisitive and demonstrate openness to innovation including AI to explore better processes and ways to alleviate friction and improve patient and client experiences Requirements - This is a remote position; however, candidates must be willing and able to travel to and work onsite at client, temporary, or corporate office locations as business needs require Benefits - Comprehensive benefits package designed to support the physical, emotional, and financial health of you and your family, including healthcare, time off, retirement, and well-being programs - Investment in professional development with opportunities for professional certification and tuition reimbursement - Quarterly and annual incentive programs for all employees who go beyond and keep raising the bar for themselves and the company

• The Data Engineer II provides a broad range of data engineering functions including data modeling, data quality, data profiling, data acquisition and ingestion, extract transform load (ETL), metadata enrichment and management, data provenance and lineage, and other specialized data management functions. • Performs complex technical tasks independently • Designs and develops to meet business and technical requirements; analyzes, adapts, integrates, codes, tests, debugs, and executes. • Leads the development of new patterns where necessary • Ensures data is maintained in compliance with enterprise data standards, policies, and guidelines.

Role Description We're looking for a Geospatial Data Engineering Intern to help build and scale our geospatial data infrastructure over the summer. This role is designed for a junior or senior undergraduate with at least one prior internship under their belt, strong data engineering instincts, and the team awareness to ship work in a shared production codebase. You'll get hands-on experience building pipelines that ingest, transform, and serve geospatial data with exposure to AI agent tooling along the way. This role begins as a full-time, 3-month summer internship and then continues part-time through September. You'll work directly with our data team, contributing to operational infrastructure that powers geospatial analysis and decision-making across the organization. Your primary focus will be building reliable, well-documented data pipelines with a geospatial backbone, while getting meaningful exposure to applied AI systems and helping us complete an in-flight migration from Airflow 2 to Airflow 3. Qualifications - Currently a junior or senior undergraduate (or higher) in Computer Science, Data Science, GIS, Geospatial Engineering, Software Engineering, or a related field - At least one prior internship (or equivalent team-based engineering experience); you've shipped code in a shared repo, taken a code review, and worked a ticket end-to-end - Available to work full-time for 3 months during the summer, then part-time through the fall semester Requirements - Strong fundamentals in Python, including classes, inheritance, decorators, type hints, and explicit imports - Strong fundamentals in SQL: joins, CTEs, window functions, and aggregations - Comfortable working in Git/GitHub with a dev → main PR-to-deploy workflow - Comfortable on the Unix command line or eager to learn (bash, navigating a filesystem, running scripts) - Familiarity with geospatial concepts (CRS, spatial joins, indexing) and tooling such as PostGIS, GeoPandas, or QGIS is a plus - Exposure to AWS or similar cloud providers; ideally S3, IAM, Athena, Glue, ECS, or Redshift - Experience with Airflow or similar orchestration tools is a plus (or strong eagerness to learn quickly) - Familiarity with dbt, Pandas, or Parquet/columnar data is a plus - Exposure to AI agent architectures (e.g., ReAct) and protocols (A2A, MCP, AG-UI) is a plus Benefits - Competitive hourly wage - $35-$40 per hour - 100% remote work from home - Opportunities to learn and grow – all things startups - A chance to play a role in defining the roadmap as we pursue a bold vision and a big goal - Work from (almost) anywhere. Ready is a remote-first company, but for security and compliance reasons, employees are not permitted to work from China (excluding Hong Kong, Macau, and Taiwan), Russia, Iran, or North Korea. - To get away - we all convene 1-2x a year for [optional, encouraged] retreats - The charter to realize a market that is set to receive $65 billion in grant funding across the United States

• Lead strategy and execution for the interoperability data platform, including ingestion, implementation, storage, structure, governance, and ongoing scalability. • Own end-to-end platform performance across data onboarding, operational readiness, platform reliability, and downstream usability. • Ensure interoperability data is organized and managed for full utilization across structured, unstructured, and emerging data types. • Drive implementation of new data channels from intake through production readiness, operational transition, and usable integration into enterprise workflows. • Partner across product, channel, technology, operations, compliance, and business teams to ensure platform capabilities support real business use cases and measurable value. • Oversee platform processes that support data intake, transformation, normalization, storage, retrieval, and downstream enablement. • Ensure data in the platform is accessible, usable, governed, and aligned to enterprise needs across operational, product, reporting, and analytic use cases. • Define and manage KPIs for platform performance, implementation success, data quality, usability, reliability, and operational effectiveness. • Identify risks, dependencies, and execution barriers early, and drive resolution across internal and external teams. • Lead cross-functional execution for platform initiatives tied to new data sources, enterprise priorities, and interoperability enablement. • Support vendor, partner, and internal technology relationships tied to platform capabilities, data utility, and delivery. • Lead and develop a strong team across implementation, platform operations, and execution management. • Continuously assess platform needs, enterprise demand, and new channel requirements to strengthen performance and increase the usable value of data over time.