Data Warehouse Architect / Data Engineering Lead

Value Innovation Labs; Lansing (MI); Hybrid; USD 140,000 - 200,000; Full time

Job summary

Value Innovation Labs is seeking an experienced Data Engineering Lead / Data Warehouse Architect to drive the design and implementation of scalable data pipelines and enterprise data platforms. The role focuses on building Lakehouse-based architectures, governance, and data products while coordinating cross-functional teams.

The ideal candidate will lead cloud data infrastructure efforts across AWS/Azure/GCP, utilize Databricks, PySpark, Spark, Python, SQL, and R, and mentor engineers to adopt

Qualifications

• Deep experience in enterprise data architecture and data engineering leadership.
• Hands-on with cloud platforms (AWS/Azure/GCP).
• Proficient in Databricks, PySpark, Spark, Python, SQL, and R.

Responsibilities

• Lead the design, development, and maintenance of enterprise data pipelines.
• Architect scalable cloud-based data infrastructure and Lakehouse solutions.
• Ensure data quality, security, governance, and data-sharing policies.
• Lead and mentor data engineering teams.
• Collaborate with data scientists, analysts, and business stakeholders.
• Drive data engineering standards and best practices.

Skills

Data Engineering

Data Modeling

DW Architecture

Cloud Platforms

Databricks

PySpark

Python

SQL

R

Lakehouse Architecture

ETL/ELT

Data Governance

Data Lake

Parquet

Kafka

Kubernetes

Docker

NoSQL

Graph DBs

AWS Glue

Redshift Spectrum

SageMaker

Power BI

Tableau

DBT

Terraform

GitHub

Jenkins

YAML

DAGs

Tools

Python

SQL

Spark

Job description

WE ARE HIRING! | Data Warehouse Architect / Data Engineering Lead (Level 3)

Work Mode: Hybrid – Wednesday & Thursday mandatory onsite

Occasional travel to Ann Arbor for in-person meetings.

Job Overview

We are looking for an experienced Data Engineering Lead / Data Warehouse Architect with strong expertise in enterprise data architecture, Lakehouse, Medallion Architecture, data engineering, and AI/ML data products.

The ideal candidate will lead the design and development of scalable data pipelines, optimize cloud data infrastructure, and drive data engineering best practices.

• Strong experience in Data Engineering, Data Modeling & Data Warehouse Architecture.
• Hands-on experience with AWS / Azure / GCP cloud platforms.
• Expertise in Databricks, PySpark, Apache Spark, Python, SQL & R.
• Strong understanding of Lakehouse & Medallion Architecture (Bronze, Silver, Gold).
• Experience building ETL/ELT pipelines from relational, semi-structured & unstructured data sources.
• Expertise in Data Lake, Parquet, Data Products & Data Governance.
• Experience with Kafka, Kubernetes, Docker & NoSQL / Graph Databases.
• Knowledge of AWS Glue, Redshift Spectrum & SageMaker.
• Experience with Power BI / Tableau and Star Schema data modeling.
• Familiarity with DBT, Terraform, GitHub, Jenkins, YAML & DAGs.

Key Responsibilities

• Lead the design, development, and maintenance of enterprise data pipelines.
• Architect scalable cloud-based data infrastructure and Lakehouse solutions.
• Ensure data quality, security, governance, and data-sharing policies.
• Lead and mentor data engineering teams.
• Collaborate with data scientists, analysts, and business stakeholders.
• Drive data engineering standards and best practices.

Preferred Qualifications

• Experience implementing enterprise Data Governance and Data Security policies.
• Strong DevOps and automation experience for data pipeline deployment.