Senior Data Engineer

Senior Data Engineer CMG Financial is moving its reporting and analytics off a legacy servicing data warehouse and onto a governed, Snowflake-based Enterprise Data Warehouse (EDW). The EDA team builds and runs that platform: ingestion from our loan-origination and servicing systems, the Raw and Bronze layers, the orchestration that keeps them fresh, and the access, classification and masking controls that let Servicing, Lending, Marketing and Domo users read the data they are entitled to, and nothing more. As a Senior Data Engineer you will be a hands-on builder on that platform. You will own pipelines and platform components end to end, from source to consumer, and treat access control, data classification and change review as part of the engineering, not an afterthought. You will work alongside the EDA engineers, domain data owners, security and compliance, and the application and DBA teams whose systems feed the warehouse. What you will do 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. What you bring 7+ years in data engineering, with 3+ years building production pipelines on a cloud data warehouse, Snowflake preferred. 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. Nice to have Mortgage, lending or loan-servicing domain experience (origination, servicing, investor reporting). Azure: Data Factory, ADLS, Key Vault, Entra ID groups and SCIM pro…

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