Data Engineer III - Data Engineering, Databricks, Model Serving, ML Ops, Data Architecture and modeling

- Designs and implements data architecture and data modeling strategies, including dimensional models, lakehouse structures, and schema design that balance performance, scalability, and governance requirements. - Writes and optimizes complex SQL queries for data transformation, validation, and performance tuning across large datasets. - Supports ML model serving infrastructure, ensuring trained models are deployed, monitored, and scaled reliably in production environments.

- Holds direct, production-level experience with Databricks, including notebooks, Delta Lake, job orchestration, and cluster management. - Demonstrates solid command of SQL, with the ability to write, debug, and optimize complex queries against large and varied datasets. - Brings practical experience supporting ML model serving infrastructure in production environments.

- Possesses a strong foundation in data architecture and data modeling, covering both operational and analytical data store design. - Has working knowledge of cloud data platforms such as AWS, Azure, or GCP. Maintains proficiency with version control systems such as Git. - Demonstrates hands-on exposure to distributed computing, cloud data platforms, and production ML infrastructure.

- Builds and optimizes both batch and streaming data workloads to support analytical and operational use cases. - Develops, optimizes, and productionizes notebooks, jobs, and workflows within the Databricks environment to support analytical and machine learning use cases. - Designs and implements data architecture and data modeling strategies, including dimensional models, lakehouse structures, and schema design that balance performance, scalability, and governance requirements.

- Demonstrates strong, hands-on proficiency with Apache Spark or comparable distributed compute frameworks, including experience optimizing jobs for performance and cost at scale. - Holds direct, production-level experience with Databricks, including notebooks, Delta Lake, job orchestration, and cluster management. - Demonstrates solid command of SQL, with the ability to write, debug, and optimize complex queries against large and varied datasets.

Preferred qualifications, capabilities, and skills - Experience with workflow orchestration tools such as Airflow or Databricks Workflows. - Familiarity with containerization technologies such as Docker and Kubernetes.

- Experience with workflow orchestration tools such as Airflow or Databricks Workflows. - Familiarity with containerization technologies such as Docker and Kubernetes. - Exposure to CI/CD pipelines applied to data and ML workflows. Familiarity with feature stores and model registries such as MLflow.

- Exposure to CI/CD pipelines applied to data and ML workflows. Familiarity with feature stores and model registries such as MLflow. - Experience with real-time streaming architectures such as Kafka and Spark Structured Streaming. - Prior experience in financial services or other highly regulated industries, with an understanding of data governance and compliance requirements.

- Familiarity with containerization technologies such as Docker and Kubernetes. - Exposure to CI/CD pipelines applied to data and ML workflows. Familiarity with feature stores and model registries such as MLflow. - Experience with real-time streaming architectures such as Kafka and Spark Structured Streaming.

Job responsibilities - Architects and maintains large-scale data pipelines using Apache Spark and related distributed computing frameworks, ensuring efficient processing of structured and unstructured data. - Builds and optimizes both batch and streaming data workloads to support analytical and operational use cases.

- Formal training or certification on data engineering concepts and 3+ years applied experience. - Demonstrates strong, hands-on proficiency with Apache Spark or comparable distributed compute frameworks, including experience optimizing jobs for performance and cost at scale. - Holds direct, production-level experience with Databricks, including notebooks, Delta Lake, job orchestration, and cluster management.