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Lead the design and delivery of a cloud-native healthcare data platform using AWS, Apache Iceberg, and modern data tools, while mentoring engineers and ensuring data reliability.
Build and maintain backend services for payment and reconciliation platforms using Java/Kotlin, Spring Boot, and AI-assisted development practices.
Build and maintain a real-time, event-driven data platform using Kafka, Kafka Streams, and ksqlDB, with Java/Spring Boot microservices, GraphQL, and MongoDB integrations.
Build and maintain scalable data pipelines and analytical datasets on AWS using Apache Iceberg, Glue, Athena, dbt, and Airflow to support healthcare analytics and operational use cases.
Build and maintain a scalable, event-driven data platform using Apache Kafka, Java/Spring Boot, and Kafka Streams for a banking client, integrating with MongoDB, S3, and GraphQL.
Develops and designs data pipelines, bridges business needs with technical solutions, and builds streaming/data processing systems using Kafka, MongoDB, GraphQL, and Java/Spring Boot for a banking client.
Build and operate a scalable, self-service data infrastructure platform on Kubernetes and Azure, using Terraform, Kafka, Iceberg, Trino, and Airflow to enable reliable, governed data access for teams.
Principal engineer designs and owns a lakehouse engine (Iceberg/Trino) that replaces cloud data warehouses in on-premise healthcare deployments, setting architecture, standards, and SQL dialect strategy.
Design and optimize large-scale data pipelines and cloud infrastructure for a global bank, using PySpark, Databricks, and Snowflake, while supporting AI initiatives like RAG and Agentic systems.
Design and maintain scalable data pipelines for streaming and batch processing using Apache Pinot, Iceberg, Flink, and Spark to power Webex analytics.
Lead the design and implementation of high-scale data architectures for Cisco's Partner Hub analytics ecosystem using Apache Pinot, Iceberg, Flink, and Spark.
Builds and maintains Java/Spring Boot applications on AWS for JPMorgan’s post-trade systems, focusing on scalable, secure code and CI/CD pipelines while adopting AI-assisted engineering practices.
Tackling complex technical challenges focused on scalability, reliability, and speed Maintaining a high-performance infrastructure that processes millions of events with low latency Collaborating with Data Scientists…
Designs and builds scalable big-data pipelines in the Hadoop ecosystem (Cloudera, Spark, Iceberg, Airflow) to process and store massive transaction datasets for Mastercard’s global payments platform.
Build and maintain scalable data pipelines and curated datasets using PySpark, Python, and SQL to power analytics and AI products for a global payments company.
About Agile5: Agile5 Technologies, Inc., is a Woman-Owned Small Business (WOSB) and Information Technology (IT) services firm that specializes in the design, development, testing, integration, and maintenance of…
Build and automate secure banking infrastructure using Kubernetes, AWS EKS, and Terraform; monitor systems with Prometheus/Grafana and support on-call reliability.
Build and scale Trino/Iceberg-based data pipelines for Wise’s finance team, transforming raw financial events into audit-ready reports using Medallion Architecture and Kafka.
Lead engineering for a secure, scalable KYC and risk data platform, designing AI/ML systems and agentic workflows in Python/PySpark on AWS/Azure/GCP.
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