*** In Person Interview *** Very long term project; initial PO for 1 year and usually the project goes for 3-5 years with this customer *** Hybrid*** 2 days a week onsite****
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:
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.
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.
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
