Lead Data Engineer

MasterApp Labs; Lansing (MI); Hybrid; USD 83,000 - 165,000; Part time

Benefits offered by this job

401(k) matching

Dental insurance

Health insurance

Vision insurance

Job summary

MasterApp Labs in Lansing, MI is seeking a Lead Data Engineer for a hybrid contract role. You will lead the design and delivery of data pipelines and ML-ready data products, working with DataBricks, SageMaker, and other tools to support analytics and operational systems.

The role requires 4+ years of hands-on data engineering experience across cloud data platforms, data governance, and DS/ML pipelines. Occasional travel to Ann Arbor may be required; in-office on Wednesdays and Thursdays.

Qualifications

• 4+ years hands-on experience building data pipelines extracting and loading data into a Data Lake on a CSP (AWS/Azure/Google).
• 4+ years transforming datasets to deliver data products with traceability to data elements in systems of record.
• 2+ years enforcing data sharing policies to data products so users see only authorized versions.

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 through standards, monitoring, governance, and validation.

Skills

Data Lake

ETL/ELT

Data Governance

Python/Spark

Cloud CSP

Education

Bachelor's degree

Tools

DataBricks

SageMaker

Power BI

Tableau

Kubernetes

Job description

Benefits:

• 401(k) matching
• Dental insurance
• Health insurance
• Vision insurance

Job Title:

Lead Data Engineer

Location:

Lansing, MI, (Hybrid)-Wednesday and Thursday are REQUIRED in-office days

Position Type:

Contract

Interview Mode:

1st round MS Teams & 2nd round In-person

Additional Requirements:

Please include 2 professional references attached as a SEPERATE document. Must include their name, company they worked together at, title, phone number, and email

Please note, travel outside of the weekly commute is required for this role: Occasionally traveling to Ann Arbor for in-person meetings. Nothing you need to attach to the submission for this, just make sure your candidates are aware and ok with this before submitting.

Required Education:

• Bachelors Degree

Required Skills:

• 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.
• 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.
• Minimum of 2 years 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.
• The Data Engineer Lead serves as the primary State of Michigan technology lead engaging with the UoM proof of concept team to support best practice adoption within the state hosting 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

Flexible work from home options available.