Senior Data Engineer

CMG Financial is moving its reporting and analytics from a legacy servicing data warehouse to a governed, Snowflake-based Enterprise Data Warehouse. The Senior Data Engineer will build and operate end-to-end data pipelines and platform components, while implementing security, governance, reliability, and orchestration practices and collaborating with data owners, security, compliance, application, and DBA teams.

Responsibilities:

• Ingestion. Build and operate ingestion from on-premises SQL Server systems (including the BytePro loan-origination system) and SaaS and vendor sources into Snowflake, using Fivetran, CDC / Change Tracking, Azure Data Factory, and vendor data shares
• Orchestration. Develop and maintain Dagster (Dagster+) assets, schedules, sensors and checks in Python. Migrate remaining GitHub Actions-run and legacy SSIS jobs onto the orchestration platform
• Transformation. Write and review dbt models, tests, seeds and snapshots for the Raw and Bronze layers, and the contracts that domain teams build on for Silver
• Platform as code. Manage Snowflake as code: databases, roles, grants, warehouses, service users and policies in Pulumi (TypeScript), Azure resources in Terraform, and deploys through GitHub Actions with reviewed, gated promotion across DEV, QA, UAT and PROD
• Security and governance. Implement role-based access, tag-based column classification and dynamic masking. Build least-privilege service accounts with key-pair authentication, and just-in-time elevation for sensitive data under GLBA, FCRA and HMDA obligations, with the approvals recorded and auditable
• Reliability. Build freshness, volume and schema checks, alerting and runbooks. Find and fix silent failures such as stalled pipes, stale grants and untagged objects before a consumer does
• Engineering practice. Contribute to specs and architecture decision records (ADRs), write clear PRs, and give and take rigorous code review. Measure before asserting, and leave a verifiable trail
• Collaboration. Partner with Servicing, Lending and Marketing data owners and with Domo and BI developers during the SDW-to-EDW migration, including parallel-run reconciliation against the legacy system
• Mentoring. Mentor engineers on the team and raise the bar on testing, automation and documentation

Requirements:

• 7+ years in data engineering, with 3+ years building production pipelines on a cloud data warehouse
• Expert SQL, and strong Python for pipeline and platform code, with tests
• Hands-on dbt experience in production: modeling, testing, CI, and environments
• Experience with a modern orchestrator (Dagster, Airflow or Prefect), and with managed ingestion or CDC (Fivetran or similar)
• Infrastructure-as-code experience (Pulumi, Terraform or similar) and CI/CD with GitHub Actions or Azure DevOps
• Snowflake security depth: RBAC design, masking and row-access policies, tags, service authentication and cost-aware warehouse management
• Working knowledge of SQL Server as a source system: CDC and Change Tracking, and reading execution plans well enough to work productively with a DBA team
• Experience handling regulated or sensitive data (PII, financial data) with auditable controls
• Clear written communication: you can write a spec, a PR description and an incident note
• Snowflake preferred
• Mortgage, lending or loan-servicing domain experience (origination, servicing, investor reporting)
• Azure: Data Factory, ADLS, Key Vault, Entra ID groups and SCIM provisioning
• Snowflake Iceberg / catalog-linked tables, secure data sharing and reader accounts
• Data catalog and lineage tooling (OpenMetadata / DataHub or similar)
• Migrating SSIS packages or legacy ETL onto modern tooling
• Domo or other BI platforms as a downstream consumer of the warehouse
• Experience using AI coding assistants responsibly in a reviewed engineering workflow
• Streaming and event-driven data: Kafka (or a similar platform such as Azure Event Hubs), change-data streams, and Snowflake streaming ingestion (Snowpipe Streaming) alongside batch pipelines
• Durable workflow orchestration and container platforms: Temporal (CMG is adopting it, and this role will integrate the data platform with it) and Kubernetes for running containerized services
• Data modeling experience with one or more established approaches: Inmon (enterprise, normalized), Kimball (dimensional), Medallion (Bronze / Silver / Gold) or Data Vault 2.0, and the judgment to pick the right one for each layer
• Experience with an AI-Driven Development Lifecycle (AI-DLC): AI agents working inside a structured, human-governed lifecycle of intent, requirements, design, implementation, testing and deployment, with each step checked and approved by an engineer before it lands