Senior Data Engineer

Eliassen Group; United States; A distancia; USD 101.000 - 162.000; Jornada completa

Descripción de la vacante

Eliassen Group is seeking a Senior Data Engineer to own the governed transformation layer end-to-end. The role designs and builds Snowflake transformation layers, migrates legacy SQL views into versioned, tested dbt models with lineage, and defines conformed dimensions to unify multi-ERP environments.

The position emphasizes modeling, testing, naming, documentation, and CI/CD standards. It is hands-on with dbt, SQL, and Python, offering a full-time, permanent opportunity with competitive salary

Formación

• 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.

Responsabilidades

• 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.

Conocimientos

Data engineering

SQL

Python

Git & CI/CD

Documentation

Communication

Self-starter

Educación

Bachelor's degree in Computer Science, Information Systems, Data Engineering, or related field

Herramientas

Snowflake

dbt

Fivetran

Airflow

Dagster

Azure Data Factory

Power BI

GitHub Actions

Azure DevOps

Descripción del empleo

Description

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.

Experience Requirements

• 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.

Education Requirements

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related quantitative field. Equivalent professional experience considered.