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Build and maintain scalable data pipelines and cloud infrastructure on Google Cloud, ensuring data quality and enabling analytics for clients.
Principal Data Engineer builds and leads data pipelines and foundations using Databricks, PySpark, Python and SQL to enable an energy trading company to extract value from data.
Designs and maintains scalable AWS-based data pipelines using SQL, Spark, Kafka, and Python to process large datasets and deliver analytics for business insights.
Lead a team to build and maintain a large AWS-based data platform, designing scalable pipelines and setting engineering standards for batch and streaming data workflows.
Builds the data backbone for an AI-driven public-safety platform, integrating legacy systems, cloud services, and real customer data with ETL pipelines and infrastructure-as-code.
Lead a team of data engineers to build and maintain Zoopla’s scalable data platform using Python, SQL, AWS, Redshift, Databricks, and Airflow.
Lead the design, build and operation of a large-scale AWS data platform using Redshift, Glue, Lambda and Kinesis to deliver reliable analytics and insights for the UK rail industry.
Design and build scalable AWS data pipelines (ETL, data lakes, warehouses) using Python, Spark, and AWS services like Glue, RedShift, and Kinesis to process large datasets for clients across multiple sectors.
Build and own large-scale distributed data pipelines using Airflow/Dagster, Spark, dbt, Kafka, and AWS to power global retail analytics and decision-making.
Lead a cloud data platform team, designing scalable pipelines and warehouses in AWS, setting engineering standards, and mentoring engineers to deliver high-performance data solutions.
Build and maintain AWS-based data pipelines that parse telecom network metrics, convert to Parquet/Iceberg, and stream into Redshift/ClickHouse using PySpark, Lambda, MSK, and Step Functions.
Build secure, scalable data pipelines and AI-ready platforms for Defence customers using Python, Kafka, Spark, and Kubernetes in air-gapped environments.
Build and maintain cloud-based data pipelines and warehouses using Azure Databricks, Snowflake, and AWS services to enable AI/ML-driven insights for enterprise clients.
Build and maintain scalable data pipelines and AI-ready datasets for analytics and agentic systems using cloud platforms like AWS/Azure/GCP and tools such as Databricks and Snowflake.
Design and maintain cloud data pipelines on AWS, build ETL/ELT workflows, and optimize Snowflake data warehouses using PySpark, dbt, and SQL for analytics in regulated industries.
Design, build, and maintain scalable data pipelines and cloud-based data platforms in AWS, Snowflake, and Azure using DevOps practices, CI/CD, and infrastructure-as-code.
Build and scale Lyst’s checkout microservices in Python, integrating payments (Stripe, Klarna) and partner retail APIs to enable seamless multi-retailer orders.
Builds and scales high-performance market-data pipelines and APIs in Python, AWS, and Kafka to power systematic trading strategies for a quantitative investment firm.
Builds and maintains Python/Django services powering Lyst’s global fashion marketplace, including trend detection, payments, and checkout systems.
Build and maintain Python-based backend systems for financial optimisation, integrating trade data with AWS services and databases to deliver secure, high-performance results for global institutions.
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