Highlights
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 . 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.
Numbers & Facts
| Location | Lansing, MI |
| Industry | Computer/IT Services |
| Salary | $78.51 |
| Company Size | 100 to 499 employees |
| Year Founded | 2009 |
| Website | http://www.tekwissen.com/ |
Description
Overview:
TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. The below Client runs through fifteen executive departments which carry out the day-to-day administration of the federal government. Popular departments like Agriculture and Rural Development, Civil Rights, Environment, Health and Human Services, Natural Resources, Secretary of State. This Client has opportunities in a number of career pathways, including, but not limited to, business and administrative support, education and human services, IT and computers, medical and healthcare, natural resources, law enforcement and public safety, skilled trades and more. With positions in over 18 state departments, your perfect career fit is waiting for you at the client. This client is an Equal Opportunity Employer.
Job Title: Data Warehouse Architect 3
Location: Lansing, MI 48933
Duration: 11 Months
Job Type: Temporary Assignment
Work Type: Hybrid
Shift Timings: 08:00 AM To 05:00 PM
Job Description:
- 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:
- 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 client 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
TekWissen Group is an equal opportunity employer supporting workforce diversity.
About Company
WE THE TEKWISSEN PEOPLE
TekWissen offers you a broader portfolio of services, industry-leading solutions, and the meaningful innovations that give you greater flexibility and speed to respond to market dynamics, reduced costs and risk to improve enterprise performance, and increased productivity to enable growth.
To keep pace with global market demands, TekWissen keeps its finger on the pulse of change. Our organized approach to guiding a project from its inception to closure. Managing projects is becoming more and more important as we enter the digital era. To cope with the pace that this transition demands, a method is required to manage projects so they can yield quality work, while incorporating efficient use of time and resources.
Project involves identifying which quality standards are relevant to the project and determining how to satisfy them.
It is important to perform quality planning during the Planning Process and should be done alongside the other project planning processes because changes in the quality will likely require changes in the other planning processes, or the desired product quality may require a detailed risk analysis of an identified problem. It is important to remember that quality should be planned, designed, then built in, not added on after the fact.
Capabilities and accomplishments in one TekWissen business enhance the opportunity for success in the others. Put simply, TekWissen's unique combination of attributes promotes success.