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Lead Data Engineer
Lead a team to design and build scalable AWS-based data pipelines, warehouses, and real-time analytics using Python, Snowflake, and orchestration tools.
Data Engineer
Build and scale a cloud-native data platform in AWS (S3, Iceberg, Spark, Airflow) to process 100M+ objects with sub-500ms latency APIs for a new UK consumer credit bureau.
Principal Data Engineer
Principal Data Engineer designs and optimizes secure data pipelines, warehouses, and real-time systems for a biotech company, using Python, Spark, Kafka, and cloud platforms.
Principal Data Engineer
Principal Data Engineer designs and optimizes secure, scalable data pipelines (ETL/ELT) and warehouses for real-time/batch processing, ensuring data quality and compliance in a biotech environment.
Data Engineer
Build and maintain scalable ETL/ELT pipelines and cloud data platforms, ensuring data quality and security while collaborating with analytics and ML teams.
Staff Data Engineer
Build and scale high-throughput data pipelines and CDC systems for a logistics platform, feeding real-time insights into the product and laying foundations for AI-driven analytics.
Senior Data Engineer
Senior Data Engineer builds and optimizes Databricks-based ETL pipelines and data products for a large-scale transformation program, collaborating with analysts and architects.
Senior Data Engineer
Senior Data Engineer builds and optimizes scalable data pipelines on a Databricks Lakehouse, using Spark, Delta Live Tables, and AWS to support real-time analytics and AI initiatives in a global financial services environment.
Databricks Senior Data Engineer
Build and maintain Databricks-based data pipelines on AWS for Citco’s financial data platform, using Python, Spark, and Delta Lake to power analytics and future AI initiatives.
Senior Data Engineer | Azure Data & Power BI
Builds data pipelines and client solutions using Azure SQL Server, .NET Core, and C# for a Dublin-based software firm.
Data Engineer (All Levels)
Build and maintain scalable data pipelines and infrastructure for a talent-cloud platform, enabling analytics and AI-driven features using Python, SQL, cloud services, and streaming tech.
Senior Data Engineer, GenAI
Leads cloud-native big data pipelines and GenAI model infrastructure at a global insurer, collaborating with AI/ML and analytics teams to build scalable solutions for risk management and business insights using Spark, Kubernetes, and AWS SageMaker.
Senior Data Engineer – Lead, Databricks & Spark
Lead a small offshore team to design and optimize data pipelines using Databricks and Spark, with a focus on performance tuning and Azure cloud integration.
Senior Data Engineer (Cork)
Senior Data Engineer builds and operates Qumulo’s data warehouse, pipelines, and AI-ready analytics using Redshift/Snowflake, DBT, and Tableau to power Sales, Finance, and executive insights.
Data Engineer III: Databricks/PySpark Pipelines & AI
Designs and maintains scalable data pipelines using Databricks, PySpark, and Python to onboard datasets and ensure data quality for analytics at a global bank.
Principal Data Engineer - GenAI & Biotech Platform
Principal Data Engineer builds and optimizes secure data pipelines for GenAI and biotech platforms, using Python/Java/Scala and SQL/NoSQL.
Senior AI Data Engineer
Senior AI Data Engineer builds and maintains scalable ELT pipelines and dbt models in Snowflake to power Zendesk’s customer-facing analytics, while integrating AI tools to enhance data delivery and team productivity.
Senior AI Data Engineer
Build and maintain scalable ELT pipelines and SQL-based data models in Snowflake using dbt to power Zendesk’s analytics and AI-enabled CX products.
QA Testing Specialist Data Engineer
QA Testing Specialist-Data Engineer validates enterprise data platforms on Databricks, ensuring ETL pipelines and large-scale datasets are accurate and production-ready using advanced SQL, Python, and PySpark.
QA Testing Specialist Data Engineer
QA Testing Specialist-Data Engineer validates enterprise data platforms on Databricks, ensuring ETL pipelines and large-scale datasets are accurate and production-ready for financial analytics.