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Build and maintain data pipelines and architectures to consolidate customer data for marketing activation, using tools like Airflow, BigQuery, and dbt.
Build and maintain robust data pipelines and platforms that power analytics and business decisions across fintech, logistics, and edtech domains using SQL, ETL/ELT tools, and data warehouses.
Builds and maintains scalable data pipelines using Python, PySpark, Airflow, and cloud data warehouses on AWS/Azure to process large volumes of data.
Build and maintain robust data pipelines in GCP using Python, SQL, and tools like Airflow and Spark to ingest, transform, and monitor large-scale datasets.
Build and maintain scalable data pipelines on GCP, using Python, SQL, Spark/Scala, and Airflow to enable analytics and automation for client projects.
Senior Data Engineer builds and maintains cloud data pipelines on AWS, enforces data-quality rules, and implements Medallion architectures to ensure reliable, scalable data for analytics.
Build and optimize cloud data pipelines and warehouses (Snowflake/BigQuery) to power AI-driven analytics and BI for global consumer brands.
Design and build data pipelines and architectures to ingest, transform, and store data for clients, using Python, SQL, ETL tools, and cloud systems.
Build and maintain Cloudera’s cloud-native streaming platform (Apache Kafka, Strimzi, Kubernetes) to power real-time data applications for enterprise customers.
Build and maintain Cloudera’s cloud-native streaming analytics platform using Apache Flink and Kubernetes, enabling customers to deploy data-heavy streaming apps at scale.
Lead the design and build of enterprise-scale data platforms using cloud tools (AWS/Azure), SQL, Python, Spark, and orchestration (Airflow) to deliver reliable, analytics-ready data for BI, AI, and reporting.
Build and maintain production ML pipelines and models in Python/Spark, orchestrating workflows with Airflow and collaborating with data scientists to deliver AI-driven business solutions.
Lead the design and delivery of enterprise-scale data platforms, building scalable ETL/ELT pipelines and cloud-native warehouses to power analytics, BI, and AI across the business.
Senior Data & ML Engineer builds automated data pipelines, a central SSOT database, and AI/ML-ready data models to support reporting, analytics, and machine learning across the business.
Principal Data Engineer builds and optimizes high-performance data pipelines for a fast-growing fintech, using Azure, Snowflake, dbt, and Airbyte to power analytics and ML at scale.
Build and operate cloud-native data platforms using Python, Spark, and AWS/GCP, designing scalable ETL/ELT pipelines and modern data warehouses.
Build and deploy production ML systems: design pipelines, orchestrate with Airflow, and maintain scalable models for fraud detection, automation, and customer insights.
Build and maintain scalable data pipelines on GCP and Snowflake to feed marketing analytics and AI-driven personalization for global brands, using Matillion, Rudderstack, and Amplitude.
Build and maintain scalable Azure data pipelines (batch & real-time) using Python, PySpark, and Azure services like Synapse and Databricks to power analytics and ML.
Build and maintain data pipelines on GCP and Snowflake that feed marketing analytics and AI/ML applications, using Matillion, Rudderstack, and Amplitude.
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