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Designs and runs Azure-based big-data pipelines using Spark, Hadoop, and Azure services to ingest, process, and store data for analytics and AI projects.
Design and maintain scalable data pipelines on Databricks using Spark, Python, Scala, and SQL to collect, store, and process large volumes of data for analytics and AI workloads.
Build and maintain data pipelines and databases to collect, transform, and deliver data for analytics and reporting under supervision.
Lead a team of data engineers to build scalable data pipelines and cloud infrastructure (AWS/GCP/Azure) using Spark, Kafka, and Terraform, while collaborating with data scientists to industrialize models.
Build and maintain scalable ETL/ELT pipelines on Databricks using Spark SQL, PySpark, and Scala to feed reliable, high-performance data for analytics and decision-making.
Build and maintain scalable ETL/ELT pipelines on Google Cloud Platform, optimize BigQuery warehouses, and automate data workflows with Python, SQL, and Terraform for real-time analytics and business insights.
Design and build scalable big-data pipelines for clients, using Hadoop/Spark, cloud data services, and orchestration tools like Airflow to store, transform, and expose data.
Consultant builds and optimizes data pipelines on Databricks for clients, using Python, Scala, and SQL to enable analytics and transformation.
Build a data warehouse and analytics dashboards for a video-game studio, integrating partner APIs using Python, R, Java, or Scala.
Builds and maintains APIs and big-data pipelines for a mobility platform using Python/Scala, SQL, Spark and Kafka.
Designs and deploys robust data pipelines and governance frameworks, using SQL and Python/Java/Scala with distributed compute and CI/CD tooling.
Build and maintain scalable data pipelines on Databricks using Spark, Scala, and Python to integrate and process enterprise datasets for clients.
Build and optimize data pipelines on Databricks and AWS, modeling large datasets for clients in a consulting role.
Design and optimize modern data architectures using Spark, Scala, and cloud environments for a tech consulting firm.
Build and maintain data pipelines, transform raw data into analytics-ready assets, and deliver dashboards for business stakeholders using Python, SQL, and AWS.
Build and maintain scalable data pipelines, warehouses, and lakes for public-sector or energy clients using Spark, Snowflake, Kafka, and GCP/Azure.
Leads Snowflake migrations and pipelines for enterprise clients, mentors teams on Snowpark/Scala/Spark, and sets best practices for scalable data engineering in a hybrid cloud environment.
Designs and builds scalable data pipelines and Snowflake environments for enterprise clients, migrating legacy Hadoop systems and enforcing clean-code practices.
Lead a team building and optimizing large-scale data pipelines using Spark and Scala, designing robust data architectures for enterprise clients.
Build and maintain data pipelines, cloud infrastructure, and support data scientists in industrializing their models using technologies like Spark, Kafka, and Snowflake.
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