Senior Data Engineer

CMG Financial

10d•$130,000 - $165,000•Onsite

About The Position

CMG Financial is migrating its reporting and analytics from a legacy data warehouse to a governed, Snowflake-based Enterprise Data Warehouse (EDW). The EDA team is responsible for building and managing this platform, including data ingestion from loan-origination and servicing systems, the Raw and Bronze data layers, orchestration, and access controls for data consumers. As a Senior Data Engineer, you will be a hands-on builder responsible for end-to-end ownership of pipelines and platform components, integrating security, data classification, and change review into the engineering process. You will collaborate with EDA engineers, domain data owners, security and compliance teams, and application/DBA teams.

Requirements

7+ years in data engineering, with 3+ years building production pipelines on a cloud data warehouse (Snowflake preferred).
Expert SQL skills.
Strong Python for pipeline and platform code, including writing tests.
Hands-on dbt experience in production (modeling, testing, CI, environments).
Experience with a modern orchestrator (Dagster, Airflow, or Prefect).
Experience with managed ingestion or CDC (Fivetran or similar).
Infrastructure-as-code experience (Pulumi, Terraform, or similar).
CI/CD experience with GitHub Actions or Azure DevOps.
Deep Snowflake security knowledge: 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 ability to read execution plans.
Experience handling regulated or sensitive data (PII, financial data) with auditable controls.
Clear written communication skills for writing specs, PR descriptions, and incident notes.

Nice To Haves

Mortgage, lending, or loan-servicing domain experience (origination, servicing, investor reporting).
Azure experience: 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).
Experience migrating SSIS packages or legacy ETL onto modern tooling.
Experience with Domo or other BI platforms as a downstream consumer.
Experience using AI coding assistants responsibly in a reviewed engineering workflow.
Experience with streaming and event-driven data: Kafka (or similar like Azure Event Hubs), change-data streams, and Snowflake streaming ingestion (Snowpipe Streaming).
Experience with durable workflow orchestration and container platforms: Temporal and Kubernetes.
Data modeling experience with established approaches (Inmon, Kimball, Medallion, Data Vault 2.0) and the judgment to select the appropriate method for each layer.
Experience with an AI-Driven Development Lifecycle (AI-DLC).

Responsibilities

Build and operate data ingestion from on-premises SQL Server systems (including BytePro) and SaaS/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, migrating remaining GitHub Actions-run and legacy SSIS jobs to the orchestration platform.
Write and review dbt models, tests, seeds, and snapshots for the Raw and Bronze layers, and contracts for Silver layers used by domain teams.
Manage Snowflake as code using Pulumi (TypeScript) for databases, roles, grants, warehouses, service users, and policies; manage Azure resources in Terraform; and deploy through GitHub Actions with gated promotions across environments (DEV, QA, UAT, PROD).
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 regulations, ensuring auditable approvals.
Build freshness, volume, and schema checks, alerting, and runbooks; proactively identify and resolve issues like stalled pipes, stale grants, and untagged objects.
Contribute to specifications and architecture decision records (ADRs), write clear pull requests, and participate in rigorous code reviews; measure performance before making assertions and maintain verifiable trails.
Partner with Servicing, Lending, and Marketing data owners, and Domo and BI developers during the SDW-to-EDW migration, including parallel-run reconciliation against the legacy system.
Mentor junior engineers on the team and improve testing, automation, and documentation standards.