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Builds and maintains data pipelines using Kafka, Spring Boot, and MongoDB, and supports GraphQL APIs and observability tools like Prometheus and Grafana.
Build and maintain high-volume data pipelines using Spark and Trino for healthcare analytics, focusing on Apache Iceberg table management and performance tuning.
The Director, Platform Engineering, is the senior engineering leader responsible for the architecture, deliver, operations, reliability, and scalability of the company’s enterprise Data Lakehouse platform. This role…
Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a…
Build and maintain scalable data pipelines and lakehouse infrastructure for adtech analytics, using Python, Spark, Airflow, and Kafka to deliver sales and finance data to leadership.
Build and migrate data pipelines from Teradata to AWS Lakehouse using Databricks, dbt, Airflow, and Iceberg for a regulated financial services client.
Design and lead a modern, cloud-agnostic enterprise data ingestion framework, building scalable pipelines across GCP, AWS, and Azure while embedding governance, quality, and security controls.
Design and maintain scalable data architectures, ETL pipelines, and data lakes on AWS for a national library and archives system, using SQL, Python, and Airflow.
Build and deploy production-grade AI agent systems for portfolio companies, integrating workflows, UIs, and backend services while serving as a hands-on technical advisor.
Build GeoAI models and data pipelines to turn agronomic sensor data into predictive tools for fruit growers, using Python, FastAPI, MongoDB, PostgreSQL, and Kubernetes.
Design and build scalable AWS-based data pipelines using Python, Spark, and cloud-native services to power analytics and reporting.
Build and optimize scalable data pipelines using Python, Spark, and AWS services like Glue, EMR, and S3 to power a modern data platform for financial indices.
Role Overview We are hiring a hands-on engineer with strong capabilities in data pipelines and applied AI (GenAI) to build and support our AI-driven platform and Datalake platform. Key Responsibilities Own the…
Your Mission This position bridges the gap between business requirements and technical implementation. It encompasses the design of data pipelines, integrations, and storage solutions (databases, data warehouses, and…
Design and maintain scalable data architectures, pipelines, and lakes for a national library and archives system using AWS, SQL, Python, and Airflow.
Build and operate eBay’s high-scale streaming and messaging infrastructure, designing a lakehouse storage system and ensuring reliability for global ecommerce platforms.
Builds and optimizes the core components of Daft, a distributed data engine for multimodal AI workloads, focusing on query planning, execution, distributed scheduling, and storage integrations (e.g., Parquet, Iceberg). Works with a small team to design and implement systems for high-performance data processing at scale.
Lead the architecture and hands-on development of a next-gen streaming and analytics platform for a fast-growing fintech company, using Kafka, Spark, Flink, and Snowflake to process billions of financial events.
Builds and scales a data platform and pipelines for a trading business using Python, SQL, cloud tech, and data lakehouse tools like Apache Iceberg.
Lead a 5–7 person team to design, build, and scale low-latency data infrastructure for a high-frequency trading firm, using Python, Java, Kafka, and Delta Lake/Iceberg.
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