Senior Data Engineer

CMG Financial is building a governed, Snowflake-based Enterprise Data Warehouse platform that moves data from source systems to trusted consumer experiences. In this role, you will help own ingestion, orchestration, transformation, and security controls end to end, with a clear focus on auditable handling of regulated data. This is an opportunity to work inside a change process that uses reviewed pull requests and approval gates, where governance rules are written down as specs and ADRs and enforced through code and CI.

What you’ll do

• Build and operate ingestion from on-premises SQL Server systems (including the BytePro loan-origination system) and SaaS or vendor sources into Snowflake using Fivetran, CDC / Change Tracking, Azure Data Factory, and vendor data shares.
• 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.
• Write and review dbt models, tests, seeds, and snapshots for the Raw and Bronze layers, plus the contracts that domain teams build on for Silver.
• Manage Snowflake as code with Pulumi (TypeScript) for databases, roles, grants, warehouses, service users, and policies; manage Azure resources with Terraform, and deploy via GitHub Actions with reviewed, gated promotion across DEV, QA, UAT, and PROD.
• Implement role-based access, tag-based column classification, and dynamic masking.
• Build least-privilege service accounts using key-pair authentication and support just-in-time elevation for sensitive data under GLBA, FCRA, and HMDA obligations, with recorded, auditable approvals.
• Create freshness, volume, and schema checks with alerting and runbooks, and find silent failures such as stalled pipes, stale grants, and untagged objects before consumers are impacted.
• Contribute to specs and ADRs, write clear pull requests, and participate in rigorous code review with measurable results and a verifiable trail.
• 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.
• Mentor engineers and raise the bar on testing, automation, and documentation.

What you bring

• 7+ years in data engineering, including 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, including modeling, testing, CI, and environments.
• Experience with a modern orchestrator (Dagster, Airflow, or Prefect) and managed ingestion or CDC (for example, Fivetran).
• Infrastructure-as-code experience with Pulumi, Terraform, or similar, plus CI/CD using GitHub Actions or Azure DevOps.
• Strong 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, including CDC and Change Tracking, and the ability to read execution plans with DBA support.
• Experience handling regulated or sensitive data (PII, financial data) with auditable controls.
• Clear written communication, including the ability to author a spec, PR description, and incident note.

Tech you’ll work with

Snowflake, SQL, Python, Fivetran, CDC / Change Tracking, Azure Data Factory, Dagster, Dagster+, dbt, Pulumi, TypeScript, Terraform, GitHub Actions, SSIS, GLBA, FCRA, HMDA, SQL Server, key-pair authentication, RBAC, dynamic masking, row-access policies, tags, Azure DevOps, Airflow, Prefect, Azure resources, BytePro, Domo.

Nice to have

• Mortgage, lending, or loan-servicing domain experience (origination, servicing, investor reporting).
• Azure experience including 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 such as OpenMetadata / DataHub (or similar).
• Experience migrating SSIS packages or legacy ETL onto modern tooling.
• Domo or other BI platform experience as downstream consumers of the warehouse.
• Experience using AI coding assistants responsibly in a reviewed engineering workflow.
• Streaming and event-driven data: Kafka (or Azure Event Hubs), change-data streams, and Snowflake streaming ingestion (Snowpipe Streaming).
• Experience with durable workflow orchestration and container platforms: Temporal (CMG is adopting it) and Kubernetes for containerized services.
• Data modeling experience across established approaches such as Inmon, Kimball, Medallion, or Data Vault 2.0, with judgment to choose the right approach per layer.
• Experience with an AI-Driven Development Lifecycle (AI-DLC) where each step is checked and approved by an engineer.

Compensation and location

Location: United States (onsite)

Annual salary: $130,000 to $165,000

Actual compensation will be determined based on factors including relevant data engineering experience, information technology experience, depth of mortgage industry experience, technical skills, education, and other job-related qualifications.

Working conditions

• ADA compliant office environment with typical office equipment and computer work.
• May involve partial stationary positions and moving throughout the day.
• Flexibility to work overtime to meet project deadlines is required.