Data Warehouse Architect

RICEFW Technologies Inc; Lansing (MI); On-site; USD 140,000 - 200,000; Full time

Job summary

RICEFW Technologies Inc. is seeking a Data Engineer Lead/Data Warehouse Architect (Level 3) to drive data pipelines, data lake modernization, and data product delivery.

The role emphasizes proficiency with Spark, Kafka, Kubernetes, SageMaker, DBT, and Databricks, enabling scalable analytics and ML workflows. The candidate will lead a team to design and implement robust data architectures, govern data sharing policies, and collaborate with data scientists, product teams, and stakeholders to

Qualifications

• Bachelor's degree required.
• 4+ years of hands-on experience building enterprise data lakes on cloud providers (AWS/Azure/Google).
• 4+ years transforming data lake datasets into data products with traceability to data elements.

Responsibilities

• Oversee design, development, and maintenance of data pipelines for analytics and operational systems.
• Architect and optimize data infrastructure including storage, orchestration, and ETL/ELT processes.
• Ensure data quality, reliability, and security with standards, monitoring, and governance.

Skills

Data modeling

Lakehouse

ML product delivery

DevOps practices

Python

SQL

Spark

Kafka

Kubernetes

Docker

Education

Bachelor's degree

Tools

Databricks

SageMaker

Power BI

Tableau

GitHub

Terraform

YAML

Docker

Glue

Redshift Spectrum

DBT

Job description

Job Description: Data Engineer Lead/Data Warehouse Architect (Level 3)

Data Engineering Lead excels in data modeling, Lakehouse and Medallion Architecture concepts, and data/ML products delivery using an agile DevOps framework with a mastery of distributed data architectures essential for data, analytical and operational ML applications with demonstrated proficiency in frameworks including Spark, Kafka, Kubernetes, SageMaker, DBT, Juniper Notebooks, Docker, SQL, and visualization tools such as Power BI/Tableau with the following track record:

Required Education:

Bachelors Degree

Required Skills:

1. Minimum 4 years of hand-on experience is required in hydrating the enterprise Data Lake on a Cloud Service Provider (AWS, Azure, or Google) from relational, semi-structured and unstructured data sources by building Extract and Load Data Pipelines.
2. Minimum of 4 years hands-on experience is required in Transforming the datasets in a Data Lake on a Cloud Service Provider to deliver Data Products with traceability of their attributes / features to the Critical Data Elements in the systems of record.
3. Minimum of 2years experience is required in enforcing Data Sharing Policies to Data Products to ensure that User get to see only the version of the Data Product the User is entitled to get access to at the time of subscription.

Preferred Skills:

• Proficiency desirable in DevOps tools including Github/Jenkins/Terraform/YAML/DAGs/Docker/Databricks/SageMaker etc. for building Data/ML pipelines to extract/store data from multiple sources to parquet destinations.
• Experience desirable in promoting Data Engineering Standards and Best Practices among the community of developers to influence the enterprise towards building scalable and supportable Data/ML Products delivery and their usage by the community of data, analytics, and ML product users.
• Experience desirable in implementing Data Governance and Data Security tools available on a CSP to automate the enforcement of Data Sharing Policies at the point of subscription of a Data Product by a subscriber.

Roles and Responsibilities:

Data Engineering Lead excels in data modeling, Lakehouse, and ML products delivery using an agile DevOps framework with a mastery of distributed data architectures essential for data, analytical and operational ML applications with demonstrated proficiency in frameworks including DataBricks, Sagemaker, Spark, Kafka, Kubernetes, SQL, NoSQL DBs, Graph DBs, and visualization tools such as Power BI/Tableau.

environment and provides training to DAV data engineers. This role’s responsibilities include:

• Oversee the design, development, and maintenance of data pipelines that move, transform, and prepare data for analytics and operational systems.
• Architect and optimize data infrastructure, including storage solutions, orchestration frameworks, and ETL/ELT processes.
• Ensure data quality, reliability, and security by establishing standards, monitoring systems, and implementing validation and governance practices.
• Lead and mentor data engineering staff, providing technical guidance, reviewing designs/code, and supporting professional growth.
• Collaborate with cross functional partners, such as data scientists, analysts, product teams, and business stakeholders, to ensure data solutions meet organizational needs and strategic goals.

Other Desired Skills:

• Must have a BS in Computer Science / Data Science / Information Systems with an overall 4+ years of experience in designing and implementing data, analytics, and AI solutions for large enterprises by building data pipelines using the available data platform tools of any Cloud Service Provider (CSP) such as AWS/Microsoft/Google.
• Experience of minimum 4 years in modeling and leveraging Relational data services with demonstrated proficiency in Python/Pysparks/SQL programming and tuning SQL queries to extract data from multiple sources ( relational DB, flat files, CSV, HTML, JSON, etc) sources into Lakehouse Medallion architecture in parquet formats
• Demonstrated experience and expertise of minimum 4 years in programming/scripting languages including Python, R, Spark, etc.
• Experience in designing and delivering Star-schema based operational data store is required to enable visualization of program activities and outcomes in a visualization tool such as Power BI.

Key Programming Languages: SQL, Python, R, Spark, Terraform Scripting, DAGs Scripting, YAML scripting, Docker

Other Technologies, Concepts and Frameworks: Juniper Notebooks, NOSQL DB, Graph DB, Parquet DB, Lakehouse and Medallion Architecture concepts, Kafka, Kubernetes, Glue, Databricks, Power BI, Tableau, Redshift Spectrum, Sagemaker, DBT, Datahub, SageMaker, Machine Learning, GitHub, DevOps