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Build and maintain scalable data pipelines and platforms for enterprise clients using cloud tools (AWS/Azure/GCP), SQL, Python, and orchestration frameworks like Airflow.
Lead a team to design, build, and maintain scalable data pipelines and analytics platforms using Python, Spark, and Azure for clients in AI and devtools sectors.
Build and maintain cloud-based data pipelines and infrastructure using tools like Databricks, Spark, Airflow, and Terraform to enable AI-driven analytics and solutions for enterprise clients.
Build and maintain data pipelines and warehouses to collect, process, and serve clean data for analysis, using ETL tools, SQL, and cloud platforms.
Build and optimize data pipelines for gaming analytics, processing real-time and batch streams with Python/Java/Go and SQL.
Lead a data engineering team to build and maintain robust pipelines and warehouses for a healthcare search engine used in hospitals, primarily using Python, Prefect, and SQL.
Build and maintain core financial data pipelines (Security Master, ETF compositions, market data) using Python, SQL, Airflow, Kafka, and GCP to ensure high-quality, reliable data for quantitative trading.
Build and maintain data pipelines, optimize T-SQL queries, and automate workflows with Python and Airflow for a banking data warehouse.
Build and maintain a Databricks-based data platform on Azure, migrating legacy systems and developing PySpark/Airflow pipelines for reliable, scalable financial data pipelines.
Build and maintain cloud-based data infrastructure for clients, designing scalable pipelines with tools like Spark, Airflow, and Terraform to enable AI-driven solutions.
Build and scale a new data warehouse and pipelines for a fast-growing fintech, using SQL, Python, AWS, and Terraform to enable analytics and real-time decision-making.
Build and maintain scalable data platforms using Python, Spark, Kafka, and cloud-native tools, then embed with partner orgs to deploy ML models and analytics in production.
Design and build scalable cloud data pipelines and AI-ready architectures using Azure services to turn raw data into reliable, analytics-ready datasets for ML and decision-making.
Build and maintain a scalable data warehouse for a national pharmacy group, integrating external sources with Python, Airflow, and PostgreSQL to power internal dashboards.
Build and maintain data pipelines in Python and SQL to feed analytical systems for environmental and transport oversight, using PostgreSQL, Airflow, and modern data stack tools.
We are looking for a Software Engineer to join our Data Integration team in Amsterdam. The Data Integration team builds and operates the platform that moves data into, through, and out of Samba TV: ingestion from…
Build and optimize data pipelines and platforms (Azure, Databricks, dbt) for clients, using Python, SQL, Spark and ELT/ETL patterns to enable analytics and AI.
Build and maintain scalable data infrastructure using tools like Airflow, Spark, and cloud platforms, while collaborating with client teams to design robust data systems.
Build and maintain SendCloud’s data lakehouse and pipelines on AWS, enabling AI-driven decisions and agentic workflows for global shipping logistics.
Build and maintain data pipelines in Python and SQL to integrate and clean datasets for environmental and transport regulators, using PostgreSQL, Airflow, and DuckDB.
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