Columbus, OH
Visa: US CitizenGreen CardGC-EADH1BH4-EADTNEAD
Responsibilities Design and implement logical, physical, dimensional, and Data Vault data models, including enterprise data models. Develop scalable data engineering solutions, data pipelines, data governance frameworks, and data architecture using Data Mesh and Lakehouse Architecture. Design and support real-time data pipelines, streaming data processing, event-driven architecture, and low-latency data processing using Apache Kafka and Google Pub/Sub. Design and implement cloud data solutions on Google Cloud Platform, including BigQuery, Dataflow, Pub/Sub, and Cloud Architecture. Support AI/ML model deployment, ML inferencing, MLOps, model monitoring, model governance, and model risk management. Work with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Vector Databases. Apply MLflow and Kubeflow for ML and MLOps capabilities. Develop and integrate solutions using Python, SQL, APIs, Kubernetes, Docker, CI/CD, Apache Spark, Databricks, and Snowflake. Collaborate across Data Science, Risk Analytics, and other teams to deliver enterprise solutions and operationalize AI/ML models. Support banking and financial domain initiatives including fraud detection, risk management, AML, KYC, regulatory compliance, and data compliance. Required Skills Strong experience in Data Engineering, Data Modeling, Logical Data Modeling, Physical Data Modeling, Dimensional Data Modeling, Data Vault Modeling, Enterprise Data Models, Data Pipelines, Data Governance, and Data Architecture. Strong knowledge of Data Mesh and Lakehouse Architecture, real-time data processing, streaming data processing, event-driven architecture, Apache Kafka, Google Pub/Sub, and low-latency data processing. Strong experience with Google Cloud Platform, BigQuery, Dataflow, Pub/Sub, and Cloud Architecture. Strong knowledge of AI/ML model deployment, ML inferencing, MLOps, model monitoring, model governance, and model risk management. Experience with Python, SQL, API Integration, Kubernetes, Docker, CI/CD, Apache Spark, Databricks, and Snowflake. Strong understanding of banking and financial domain concepts, including fraud detection, risk management, AML, KYC, regulatory compliance, and data compliance. Strong cross-functional collaboration skills with experience working across Data Science, Risk Analytics, and enterprise solution initiatives. Desired Skills GCP Professional Data Engineer certification. GCP Professional Cloud Architect certification. Experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Vector Databases. Experience with MLflow and Kubeflow. Experience operationalizing AI/ML models within enterprise environments.
