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 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.

How we work

Changes land through reviewed pull requests, and production changes go through approval gates. Governance rules are written down as specs and ADRs and enforced in code and CI, so whether someone can read a column is decided by a reviewed rule, not a one-off grant. We value measuring over assuming, fixing root causes over workarounds, and owning a problem until it is actually closed.

SUPERVISORY RESPONSIBILITIES: Direct Reports: N/A

PHYSICAL and ENVIRONMENTAL CONDITIONS:

This role operates in an ADA compliant office environment, utilizing typical office equipment and tasks including computer work. The position may involve partial stationary positions and moving throughout the day. Flexibility to work overtime to meet project deadlines is required.

COMPENSATION

Annual Salary: $130,000 to $165,000. Actual compensation will be determined based on factors including relevant experience in data engineering, information technology, depth of mortgage industry experience, technical skills, education, and other job-related qualifications.

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