This Position Is Based in Ramsey, NJ. It is Not Remote!
Position Overview:
Konica Minolta is seeking a Data Engineering and Machine Learning Ops Manager to join our growing Data and Analytics team. The Manager must be familiar with Google Cloud Platform (GCP) and be technically proficient in BigQuery and dbt Cloud. He or she will build and lead a team of data engineers and data scientists. The role will be instrumental in building out our data platform and enabling the organization to gain new business insights. The ideal candidate will be comfortable working with a wide range of stakeholders and be able to collaborate across teams to solve technical challenges and drive business results.
Position Responsibilities: • Design, build, and maintain a data platform that is scalable, reliable, and resilient in order to support KMBS data products and analytics needs. • Design and optimize data pipelines to ensure timely and consistent data availability. • Ensure data is easily accessible and usable to analysts and data scientists for insights and analysis. • Resolve architectural challenges and ensure data quality and integrity so that the data is trustworthy. • Partner with the Information Security team to ensure proper handling of different types of data. • Lead, train, and guide a team of data engineers and data scientists - drive collaboration and an innovative mindset. • Review code and support team members in resolving code conflict issues. • Monitor data usage and storage to scale platform infrastructure appropriately and achieve cost optimization. • Drive the design, build, and launch of new data models and data pipelines in production. • Define the processes needed to achieve operational excellence in enabling the organization to access and leverage data for different use cases. • Develop and implement MLOps strategies, best practices, and standards to enhance AL / ML model deployment and monitoring efficiency. • Develop roadmap and strategy for MLOps and LLMOps Platforms and model lifecycle implementation. • Analyze and recommend solutions for building and operationalizing AI / ML models. • Define and develop processes and tools to monitor and analyze model performance and data accuracy.
Qualifications: • Bachelor's degree in Computer Science, Math, Physics, or related technical fields. • 8+ years of professional experience as architect, engineer, data scientist, etc. • Experience scaling and managing 3+ person teams. • Experience working in an agile delivery model. • Experience building secure and reliable cloud architectures on Google Cloud Platform (GCP). • Adept at utilizing Google Cloud Functions for serverless computing and building RESTful APIs for seamless data integration. • Experience designing and architecting scalable data systems that leverage a hybrid approach of batch processing and real-time data streaming. • Proven ability to develop custom Python connectors to bridge data pipelines with various sources. • Experience with Jenkins Automation Server and DevOps practices like CI/CD pipelines and infrastructure-as-code tools. • Technically proficient in BigQuery and dbt Cloud. • Experience working in GitLab, Bitbucket, etc. for CI/CD and version control. • Solid understanding of programming languages used in data engineering, like Python, Java, and SQL, etc. • Deep knowledge of SQL databases and ability to execute queries quickly. • Knowledge of data warehousing and data modeling. • Understanding of how to maintain ETLs operating on a variety of structured and unstructured sources. • Strong critical thinking and problem-solving skills. • Excellent written and verbal communication skills in order to explain technical concepts to non-technical stakeholders and facilitate cross-team collaboration. • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
Preferred Experience and Skills: • Strong understanding of Open Source tooling and their applications within the data ecosystem. • Familiarity with Meltano SDK. • Terraform expertise for infrastructure provisioning and management. • Proficient in working with the Command Line Interface (CLI) for efficient system administration. • Familiarity with Airflow for robust workflow orchestration and scheduling. • Experience implementing monitoring, alerting, and logging mechanisms for proactive system health checks. • Containerization & Orchestration: Utilize Docker and Kubernetes for containerized deployments and orchestration.