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Build and maintain scalable data ingestion pipelines and ETL/ELT processes for a cardiac remote monitoring platform using Python, Spark, and AWS.
Designs and maintains scalable data pipelines, architectures (ETL/ELT, data lakes/warehouses), and automated workflows using Python, SQL, Spark, Kafka, and cloud platforms (AWS/GCP/Azure) to power clients’ open-source projects while ensuring security, governance, and performance.
Build and deploy data pipelines that bridge customer warehouses to Hilbert’s AI growth engine, using ClickHouse, Snowflake, BigQuery, and orchestration tools like Dagster and Airbyte.
Builds and maintains data pipelines and infrastructure to feed AI-driven growth models, integrating tools like ClickHouse, Dagster, and Airbyte.
Build and scale the data infrastructure that powers LLM training, fine-tuning, evaluation, and RAG systems using cloud tools like AWS Glue, EMR, and Kubernetes.
Build and maintain scalable data pipelines on AWS and Databricks, focusing on ETL, real-time processing with Spark/Kafka, and data quality for a fintech/crypto company.
Build and optimize high-performance Spark data pipelines in Python, migrating legacy SQL ETL to Delta Lake and Airflow orchestration while ensuring privacy-preserving data collaboration.
Build and maintain batch and real-time data pipelines using SQL, Python, and tools like Spark and Airflow to feed analytics and operational systems.
Build and maintain ETL pipelines to move transactional data into a modern data warehouse, write SQL transformations, and ensure clean, reliable data for analytics and risk teams.
Build and govern a reliable dbt analytics layer for a fintech investor, transforming raw data into clean, actionable metrics and models for business decisions.
Build and maintain data pipelines and analytics for renewable energy supply and revenue optimization using Python, SQL, DBT, and Flyte.
Senior Platform Engineer building data infrastructure with dbt, Python, Databricks, and Dagster, plus monitoring and mentoring junior engineers.
Senior Platform Engineer builds and scales data pipelines, orchestration (Dagster), and internal tooling in Python/AWS to support healthcare analytics and AI-driven insights for cancer care.
Owns end-to-end analytical domains: defines metrics in a governed semantic layer (dbt/Snowflake), models data, and ensures consistent KPIs across dashboards and AI tools.
Designs and oversees the architecture of AI-driven automotive systems and data flows, selecting tech stacks and ensuring scalability and reliability.
Designs and builds data pipelines and multi-layer storage architectures for entertainment, gaming, and sports platforms using ETL tools, SQL, and dbt.
Senior Data Engineer builds and scales Python-based data pipelines (Pandas, PySpark, dbt, Airflow) on Snowflake/BigQuery, ensuring reliability and performance for analytics and AI workloads.
Build secure, compliant data pipelines and AI systems for medical-device analytics using Python, Spark, and cloud platforms (AWS/Azure/GCP).
Builds and maintains cloud-based ETL pipelines that process environmental datasets (energy, emissions, resources) using Python, PySpark, and AWS to power sustainability analytics.
Design and build scalable data pipelines and warehousing for AI infrastructure, using tools like Dagster, dbt, Snowflake, and Tinybird to support analytics and ML workloads.
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