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Lead data engineering efforts to migrate CRM data from Veeva to Salesforce Life Sciences Cloud into a Databricks lakehouse on AWS. Build and optimize ingestion pipelines, implement dbt transformations and Airflow orchestrations, manage Delta tables and Databricks clusters, and enforce data quality, monitoring, and CI/CD practices while collaborating across teams on US East Coast hours.

Design, build, test, and operate scalable, low-latency backend services in Java. Integrate applications with ML models and feature stores, implement resilience and observability, develop tests and SLAs, participate in incident response, and collaborate across software, ML, data, and infrastructure teams to ensure production reliability.

Build, test, and maintain scalable, low-latency Java backend services and microservices; integrate applications with ML models and feature stores; implement resilience, monitoring, testing, and observability; participate in incident response, reliability improvements, architecture and code reviews, and cross-team collaboration.

Design, build, test, and operate scalable, low-latency Java backend services and microservices. Integrate applications with ML models and feature stores, implement resilience patterns, automated testing, monitoring, tracing, and observability (Datadog/OpenTelemetry/Grafana/Prometheus). Optimize SLAs and performance, participate in incident response, RCA, architecture and code reviews, and collaborate with ML, data, infra, and product teams.

Design, build, test, and operate high-performance Java backend services and microservices integrated with ML models and feature stores. Ensure low-latency, high-availability production reliability, observability, resilience patterns, comprehensive testing, and incident response while collaborating across software, ML, data, infra, and product teams.

Seeking a Senior Data Engineer to design and implement high-performance data solutions and pipelines, ensuring scalability and reliability in cloud environments.

Design, build, and maintain AWS-based data enrichment pipelines (Spark/PySpark). Manage S3/Glue/Athena data platform, orchestrate with Airflow, perform deep data quality analysis, troubleshoot tracking and delivery issues, implement observability, and collaborate with global teams on data governance and pipeline optimization.

Design, build, and maintain scalable AWS-based data enrichment pipelines (Spark) and orchestrations (Airflow); use S3/Glue/Athena for storage, processing and querying; perform deep data quality analysis, implement validation/observability, troubleshoot tracking and delivery issues across hybrid systems, and collaborate on governance, lineage, and schema design to support media measurement and audience intelligence.

As an Applied AI Engineer, you will design, develop, and deploy AI automation solutions, integrating LLMs into business workflows, and collaborating with stakeholders. Responsibilities include building reliable systems, supporting clients, and enhancing features.

The Applied AI Engineer will design, develop, and deploy AI solutions, integrating LLMs into business workflows, and ensuring reliability and maintainability of systems.