Remote
Our client seeks a Senior Data Engineer to own the governed transformation layer end-to-end. The role will design and build Snowflake transformation layers, migrate legacy SQL views and user-maintained dimensions into versioned, tested dbt models with lineage, and define conformed dimensions and operational facts to unify multi-ERP environments. The position will establish modeling, testing, naming, documentation, and CI/CD standards. The work is hands-on with dbt, SQL, and Python.
This is a full-time, permanent opportunity, offering a competitive salary and comprehensive benefits package. Qualified applicants must be willing and able to work on a w2 basis.
Salary: $101,000 - $161,800/ yr. w2
JN -102026-109159
Responsibilities
- Design and build the Snowflake transformation layer from raw to staging to conformed to marts as the governed foundation for reporting and AI/ML.
- Migrate existing SQL views to versioned, tested dbt models with full lineage.
- Define conformed dimensions and operational facts across multi-ERP environments using Kimball principles including star schemas, surrogate keys, fact grain, and SCD Type 1 and Type 2.
- Set standards for modeling, testing, naming, documentation, CI/CD, Git workflows, code review, dbt project structure, model contracts, and release management.
- Partner with report owners to validate semantic equivalence and minimize disruption during BI cutovers.
- Define and own the semantic layer across Snowflake, dbt, and Power BI to ensure consistent metrics.
- Implement data quality testing, observability, and lineage with dbt tests and equivalent tools, partnering with IT Audit on SOX-relevant controls.
- Design models for AI/ML and agent readiness with stable keys, clear semantics, and metadata for LLM retrieval and tool use.
- Collaborate with AI/analytics teams on feature-table patterns, golden-record exposure, and data foundations for agentic applications.
- 7+ years in data or analytics engineering with 4+ years on a cloud data warehouse, preferably Snowflake.
- Expert SQL including window functions, performance tuning, and incremental load patterns.
- 3+ years of production dbt experience, including macros, tests, snapshots, incremental strategies, exposures, and documentation. Experience designing and maintaining a dbt project from scratch.
- Hands-on Kimball dimensional modeling with star schemas, conformed dimensions, surrogate keys, and SCD Type 1 and Type 2.
- Production experience with at least one ingestion tool such as Fivetran, Airbyte, Informatica, or Matillion and one orchestrator such as dbt Cloud, Airflow, Dagster, or Azure Data Factory.
- Hands-on Git and CI/CD for data using GitHub Actions, Azure DevOps Pipelines, or GitLab CI with code review and release practices.
- Proficient Python for data tooling including dbt macros, automation, data quality frameworks, and lightweight APIs.
- Experience modeling data from at least one enterprise ERP such as Oracle, SAP S/4HANA, SAP ECC, or Microsoft Dynamics.
- Strong written communication for standards, runbooks, and documentation.
- Self-starter effective in a fast-paced multi-ERP environment with minimal supervision.
- Experience supporting AI/ML, RAG, or agentic workloads from a warehouse layer.
- Preferred: Table-level experience with SAP ECC or S/4HANA finance and logistics tables, Power BI semantic models and DAX, regulated industry background, SOX ITGC familiarity, and exposure to MDM tooling.
- Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related quantitative field. Equivalent professional experience considered.