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Interview Type
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Virtual and In-Person. 1st round is virtual, 2nd round is in-person. For the virtual round, Candidates should join from a laptop and be prepared to share their screen if requested. A screenshot photo of candidate will be required for any interviews as well as a vendor present at beginning of virtual interview to validate candidate
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Note
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Please include 2 professional references attached as a SEPERATE document. Must include their name, company they worked together at, title, phone number, and email
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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.
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Hybrid Work Schedule: Wednesday and Thursday are REQUIRED in-office days. This is NON-NEGOTIABLE and there is NO remote only option.
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Local candidates vs non-local: Open to candidates local to the Lansing Michigan area and those willing to relocate. Candidates that relocate must relocate before day one and be ready to be on-site from day one at their own expense (non-negotiable)
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Clear and concise written and verbal communication is crucial to success and productivity in this role
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Job Description
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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:
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:
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Oversee the design, development, and maintenance of data pipelines that move, transform, and prepare data for analytics and operational systems.
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Architect and optimize data infrastructure, including storage solutions, orchestration frameworks, and ETL/ELT processes.
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Ensure data quality, reliability, and security by establishing standards, monitoring systems, and implementing validation and governance practices.
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Lead and mentor data engineering staff, providing technical guidance, reviewing designs/code, and supporting professional growth.
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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.
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Top Skills & Years of Experience
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Required Education: Bachelors Degree Required Skills:
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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.
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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.
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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:
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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.
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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.
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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.
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