Senior Data and BI Analyst / Developer - (Remote - LATAM)

Orchid Software Solutions  |  Remote, Latin America  |  Full-time contract

Orchid Software Solutions builds and operates software for healthcare and family-building services. Our products cover escrow for surrogacy and egg donation, agency management for fertility clinics, medical records retrieval, insurance risk management, and independent dispute resolution for healthcare payments under the federal No Surprises Act.

Our engineering team is distributed across the United States and Latin America, with a few colleagues further afield. We work remotely, in writing, and across time zones.

This is our first dedicated data and business intelligence role. You will own reporting end to end: the data model, the metric definitions, the dashboards our operations teams run on, and the reporting our enterprise clients see.

You will start with our healthcare payments dispute resolution product, which has the largest data footprint and the most immediate reporting need. Once that reporting is running, the role expands across our other products, bringing one set of definitions to metrics that are currently produced product by product.

The interesting part of this job is not the dashboards. Much of what our applications display is calculated at read time by application code rather than stored in a column, so the same question can be answered several ways. Your job is to decide what each metric means, document it, prove it reconciles against the systems of record, and defend that definition when someone wants a different number. If you like being the person whose figures nobody argues with, you will like this role.

You will work alongside an engineering team of senior fullstack engineers and QA, a product manager, and business leadership in the United States. You will present your numbers directly to executives and to client counterparts, so this is a role for someone comfortable in front of an audience, not only in a query editor.

• Build and maintain internal reporting for operational volume, case status and stage, cycle and resolution time, throughput, and service-level metrics.
• Own payment reconciliation reporting, where the tolerance for an approximate answer is zero.
• Design and ship scoped, self-service reporting for enterprise clients, so they can answer their own questions without going through our support team.
• Model data in our analytics warehouse: dimensional models, incremental transformations, and tested, documented pipelines.
• Own the metric layer. One definition per metric, the query that produces it, the business rule behind it, and a data dictionary anyone can read.
• Reconcile reporting against the source systems and keep it reconciled as the applications change.
• Work with role-scoped access and data minimization as a matter of routine. We handle regulated healthcare and payment data, and reporting is built to keep sensitive fields out of it.
• Improve data quality. Find the defects that make reporting wrong, quantify them, and drive the fixes with the engineering team.
• Turn ambiguous questions from executives and clients into defined metrics, and say so when a question cannot be answered from the data we hold.
• Extend the same patterns and definitions to our other products as the function grows.

• Expert SQL. Complex analytical queries, window functions, and query performance work. You can tell the difference between a number that is surprising and a number that is wrong.
• Hands-on warehouse experience. ClickHouse, Snowflake, BigQuery, Redshift, or Databricks. You have modeled data in one, not only queried someone else’s models.
• Dimensional and semantic modeling. You have built a reporting layer over a normalized transactional schema and owned the definitions that sat on top of it.
• Client-facing reporting, shipped. You have built reporting that external customers used, with the scoping and access controls that requires.
• You write code, not only queries. Production-quality work in Git, code review in both directions, and the ability to build and run your own transformation jobs.
• Stakeholder judgment. You can sit with an executive or a client, work out what they actually need, and push back when the ask does not hold up.
• Data governance in a regulated setting. Practical experience with role-based access, handling of personal or health data, and producing evidence for an audit. HIPAA, SOC 2, PCI, or similar.
• Clear written communication. Our team is distributed and most decisions happen in writing.
• Professional English, written and spoken. You will present to United States executives and to client counterparts. Fluency is required and is assessed in the first conversation.
• Time zone overlap. A minimum of four hours of daily overlap with United States Eastern time, 9:00 to 13:00 ET.
• Able to contract independently. You can invoice and contract as an independent professional in your country of residence, and accept confidentiality and data-handling terms covering regulated healthcare data.

• Healthcare payments, claims, or payer and provider data. Experience with No Surprises Act dispute resolution is a real advantage.
• dbt or a comparable transformation framework, in production, with tests.
• Change data capture pipelines and the failure modes that come with them.
• Financial reconciliation reporting.
• Python for data work.
• Enough TypeScript to read an application and trace how a value is calculated.
• An embedded analytics or BI tool taken to production with external users, including row-level security.
• AWS, particularly RDS, S3, and IAM as it applies to data access.
• Prior work with a United States company in a regulated industry.

• It is not a ticket queue for dashboard requests. You own definitions and you are expected to have opinions about them.
• It is not platform or infrastructure engineering. We have a platform team and you will partner with them.
• It is not machine learning or model development. That work sits with a separate team.

TypeScript and Next.js applications, MySQL and PostgreSQL as operational stores, ClickHouse as the analytics warehouse, change data capture between them, Python for data work, and AWS throughout. Our older products run on Python and Flask.