We are seeking an experienced Senior Data Engineer / Data Architect to lead backend data transformation, pipeline engineering, and relational architecture connecting large-scale enterprise data platforms to a centralized Microsoft Dataverse / Azure environment.
In this role, you will build the foundational, high-performance data backbone used by application developers and business analysts to create real-time operational apps and analytical dashboards. You will handle heavy compute transformations, build automated API egress pipelines, model clean dimensional star schemas, and enforce strict security, data quality, and role-based access controls.
If you have proven experience designing and orchestrating data pipelines across commercial enterprise data platforms such as Databricks, Snowflake, Palantir Foundry, AWS, or Azure your skills will translate seamlessly to this role.
Key Responsibilities
- Data Discovery & Pipeline Engineering: Map raw, multi-source enterprise datasets; design, deploy, and maintain automated, fault-tolerant ETL/ELT pipelines with scheduled synchronizations and automated error handling.
- Heavy Compute & Transformation: Utilize modern cloud compute engines (SQL, Python/PySpark, Databricks/Foundry-like environments) to cleanse, deduplicate, harmonize, and curate disparate raw data feeds into an authoritative single source of truth.
- Relational & Dimensional Modeling: Architect high-performance dimensional data models (Fact/Dimension tables, Star Schemas, 1:N and M:N relationships) within Microsoft Dataverse / relational stores optimized for downstream Power Apps and Power BI.
- API Integration & Egress: Build and sustain secure, automated system-to-system egress connections (REST API, OData endpoints, secure connectors) leveraging Service Principals, modern token authentication, and strict latency/uptime SLAs.
- Data Lineage & Quality Auditing: Implement programmatic validation routines and reconciliation scripts to audit source-versus-target records, ensuring minimal variance (<0.1%) and zero silent record drops. Maintain comprehensive documentation, including Data Dictionaries, ERDs, and version-controlled transformation lineage.
- Security & Access Architecture: Design and enforce enterprise-grade Role-Based Access Control (RBAC), Row-Level Security (RLS), Field-Level Security, and automated data masking/hashing for sensitive data (PII/PHI).
- Stakeholder Collaboration: Coordinate with data governance bodies, cross-functional engineering teams, and frontend developers to align data ontologies, review schemas, and resolve technical blockers.
Location, Schedule & Logistics
- Location: Camp Murray, WA (Hybrid: ~75% telework / remote, ~25% on-site).
- Schedule: Monday – Friday, 06:00 AM – 2:30 PM Pacific Time.
- Environment: Remote work performed via secure Azure Virtual Desktop (AVD); company/contractor-provided hardware with government-issued smart card (CAC) access.