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Design and build cloud-native data pipelines and platforms for clients, using Snowflake/Databricks and infrastructure-as-code tools like Terraform to enable scalable analytics and transformation.
Design and build cloud-native data pipelines and platforms for clients, using Snowflake, Databricks, and infrastructure-as-code tools like Terraform.
Designs and builds secure, scalable cloud data pipelines and models for digital transformation, focusing on ingestion, processing, storage, and analytics while collaborating with cross-functional teams.
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.
Senior Data Engineer builds and maintains scalable batch/streaming pipelines (PySpark, SQL) for EY GDS Spain, supporting clients’ digital transformation with distributed data processing and ETL/ELT workflows.
Build and maintain Preply’s scalable data lake and real-time ingestion pipelines, ensuring high-quality, governed data assets for analytics and ML across 180+ countries.
Build and maintain AWS-based data pipelines and Data Lake frameworks using Spark, Python, and Kafka to process batch and real-time data at scale.
Build and run scalable data pipelines for a healthcare platform serving 90M+ patients, using Python, Airflow, AWS, and Kubernetes to enable analytics and AI across 13 countries.
Leads the technical roadmap for a healthcare data platform’s data ingestion, MDM, and remediation engines, focusing on FHIR-based pipelines, Spark/Databricks architectures, and scalable patient record matching.
Lead the product vision for Smile’s high-throughput healthcare data platform, transforming raw clinical records into clean, unified patient records using Apache Spark, Databricks, and FHIR-based MDM systems.
Builds and optimizes data pipelines, storage systems, and analytics platforms using Python, Spark, SQL, and cloud tools (AWS/Snowflake) to enable real-time insights and predictive modeling for a global financial services firm.
Designs and maintains scalable data pipelines using Spark, Kinesis, Kafka, and AWS to ingest, transform, and deliver high-quality data for analytics and modeling.
Build and optimize scalable data pipelines using Python, SQL, Spark, and cloud tech to enable prescriptive and predictive analytics for a fintech company.
Build and maintain robust data pipelines, implement automated quality checks, and ensure reliable data flows for enterprise clients using Python/TypeScript, SQL, and AWS.
Build and maintain AI-driven data pipelines, implement quality checks, and collaborate with teams to ensure reliable data flow for adtech clients.
Leads LLM/NLP model development and builds production-grade AI systems with robust data pipelines for Earth data solutions.
About the Company: Our client is a global MNC that has a strong presence in Asia, they are looking to hire for a Senior Data Engineer specializing in AWS services and AI platforms. Role & Responsibilities: Build…
Build and maintain data pipelines, ETL jobs, and reporting dashboards using Cloudera, SQL, Python, PySpark, and Tableau.
Designs and maintains scalable data pipelines and warehouses for mission-critical systems, integrating real-time and batch data with Java, Python, Spark, and SQL.
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.
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