Risk and Quality Analytics Engineer
Overview
We are seeking a hands-on Senior Engineer to own the architecture, build, and reliability of the data and integration platform supporting Risk Adjustment and Quality operations. This role is responsible for the production engineering work behind file ingestion pipelines, validation engine logic, third-party integrations, and the BigQuery and Dataform data layer that drives downstream operations and reporting.
The ideal candidate is a strong software and data engineer with deep production experience on Google Cloud, fluent Python, and a high bar for code quality, observability, and security. This role serves as the technical anchor for the team, partners closely with the Risk & Quality AO Operations Lead to translate operational requirements into durable engineering, and provides code review and technical guidance to the Operations Engineer.
Key Responsibilities
Data Pipeline Architecture & Build
- Design, build, and own production data pipelines on Google Cloud, including:
- Per-client ingestion notebooks moving files from transport layer into Cloud Storage and BigQuery
- Dataform workflows for validation, transformation, and downstream modeling
- Event-driven processing tied to Cloud Storage triggers
- Establish and enforce standards for pipeline structure, error handling, retries, logging, and configuration management.
- Drive performance and cost discipline on BigQuery through partitioning, clustering, slot awareness, and query review.
Validation Engine & Data Modeling
- Build and evolve the file validation engine in Dataform, including deterministic checks (schema, referential integrity, file integrity) and statistical checks (volume, null rate, distribution drift).
- Implement severity-tiered handling (PASS, INFO, WARN, HOLD) with consistent downstream signaling into Jira and reporting layers.
- Design and maintain the BigQuery data model for validation results, ingestion runs, file configuration, and reconciliation outcomes.
- Partner with the Operations Lead on threshold setting, baseline periods, and statistical model selection.
Integration Engineering
- Build and maintain client and vendor data integrations for inbound files, outbound delivery, archive, and reject workflows.
- Engineer reliable transport patterns including authentication, retry semantics, pagination, and rate-limit handling against third-party APIs and file systems.
- Develop reusable Python libraries for transport, validation, and observability that are consumed across per-client notebooks.
Security, IAM & Compliance Engineering
- Own service-account, IAM, and Secret Manager configuration for the platform; enforce least-privilege access.
- Apply HIPAA minimum-necessary principles to data flows, access patterns, and logging; partner with the Operations Lead on quarterly access reviews.
- Implement and maintain credential rotation, audit logging, and evidence-capture practices that support audit readiness.
Reliability, Performance & Observability
- Define and implement observability for pipelines (logs, metrics, structured run records); make failures discoverable and debuggable.
- Lead on production incidents impacting the platform; perform root-cause analysis and own corrective fixes.
- Track and improve platform reliability metrics (success rate, latency, time-to-recover) with measurable targets.
Code Review & Technical Mentorship
- Review pull requests from the Operations Engineer and external contributors; enforce standards for testing, error handling, and security.
- Provide technical mentorship and pairing on Python, BigQuery, and GCP best practices.
- Author technical design documents and architecture decision records for significant changes.
FAQs
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