Contract Microsoft Azure Data Engineer Jobs in Michigan

The Data Science and Analytics Center of Excellence (COE) is responsible for leading the creation and development of the overall strategy and direction of data science and advanced analytics – including ensuring continuity and seamless extension of existing programs, the development of a short- and long-term vision and roadmap, and defining and institutionalizing the role that data and analytics play throughout the organization as the fuel that drives and shapes client’s priorities and serves as an accelerant for client’s progress.

The ML Engineer is a key player in the Integrated Tech Programs & Strategies team. This role will be responsible for data engineering, data science Model deployment, testing and management for the end-to-end ML and data pipeline including data products. This role will leverage Client’s AI labs environment to enable the delivery in a common data lake and products.

• Responsible for building and managing end-to-end data pipelines and operations from ingestion and integration through delivery for the data science prototypes and data products.
• Adept at queries, report writing and presenting findings, analyze large complex datasets to extract insights and decide on the appropriate technique.
• Understand and use data and ML fundamentals, including data structures, algorithms, computability and complexity and computer architecture.
• Collaborate with data engineers to build data and model pipelines, manage the infrastructure and data pipelines needed to bring code to production.
• Provide support to engineers and product managers in implementing machine learning in the product.
• Drive the design, building and launching of new data models and ML/Data pipelines in production.
• Identify, analyze, and interpret trends or patterns in complex data sets.
• Consulting with managers, Product owners to determine and refine machine learning objectives.
• Transforming data science prototypes and applying appropriate ML tools and technologies.
• Contribute and support the development of the overall data science and machine learning strategy and roadmap.

• Bachelor’s degree in computer science, or equivalent IT knowledge/experience.
• 2+ years of relevant work experience in Data Analysis, Data Engineer, Data Science & Data Integration.
• Must have strong data infrastructure, data engineering and Machine Learning skills
• Must have a proven track record of leading and scaling data pipelines, ML Model deployments in a cloud/on prem/big data environment
• Strong knowledge of and experience with reporting packages (Business Objects etc.), databases (SQL etc.), programming (XML, JavaScript, or ETL frameworks).
• Programming languages: SQL, Spark, Python, R, Jupyter Notebooks, Java, Scala, C++
• Data Exploration and ETL: Alteryx, Talend, H2O, Informatica, Data Stage, Azure Data explorer, Azure Data Factory.
• Data Warehouse Solutions: Redshift, Snowflake, Postgres, Data Lake.
• Big Data technologies: Azure, AWS, Hadoop, Spark, Hive, Kafka, Flume, NoSQL stores (HBase, Cassandra, DynamoDB, MongoDB).
• Cloud storage: S3, GCS, ADLS, Blob.
• Machine Learning: Cloudera Data Science Workbench, Azure ML, Amazon ML, Google AutoML, Vertex AI.
• Data Visualization Solutions: MS Power BI, Looker, Tableau, Azure Streaming Analytics, Data Lake Analytics, Azure Time Series Insights, Azure Synapse Analytics.
• CI/CD and Code Management: Git, Maven, Docker, Jenkins, Azure Dev Ops.
• Experience working with Data engineering, Data science, ETL teams and managing implementing projects that utilize big data, advanced analytics, and machine learning technologies.
• Hands-on experience in building data and ML pipelines from variety of sources such as data warehouses and in-memory OLAP models, as well as experience in NoSQL/cloud.
• Strong understanding of data, ML Models, Big Data, Relational databases, streaming and batch data processing.
• Knowledge of machine learning evaluation metrics and best practice.
• Strong experience building end-to-end data view with focus on integration.

• Experience working with on-prem and cloud-based data warehouses
• Experience with cloud-based personalization and machine-learning applications.
• Experience working for consumer or business-facing digital brands.