Tech jobs
Job listings
Data Analyst (Marketing)
Analyze marketing data for a travel metasearch engine, build dashboards, run A/B tests, and maintain data pipelines using SQL, Python, and BI tools.
Senior AWS Data Engineer
Design and build secure, governed AWS data lakes and pipelines for a global investment firm, using Python, PySpark, Athena, Iceberg, and Terraform.
Data Engineer AWS
Designs and builds modern AWS-based data platforms, including pipelines, data lakes, warehouses, and lakehouses, using Python, Spark, and AWS services to help clients extract value from their data.
Junior Data Engineer - Data Management AI & Analytics
A junior data engineer working on data management, AI, and analytics tasks, including identifying datasets, onboarding data into the enterprise data lake, transforming data, creating reporting models, and collaborating with teams to implement business logic and manage use cases.
Product Analyst (Mid/Senior) – Employment Contract (UoP)
Analyze product data to shape decisions, define metrics, and run experiments using SQL, Python, and BI tools in a SaaS environment.
Senior Data Engineer
Build and optimize cloud data pipelines using Azure/AWS and BI tools, then present insights to consulting teams across industries.
Senior Data Engineer
Build and maintain cloud data pipelines and BI reports for EY’s global consulting clients using Azure, Databricks, and BI tools.
Databricks Data Engineer Delta Live Tables Specialist
Builds and optimizes data pipelines using Databricks, focusing on Delta Live Tables, Auto Loader, and Unity Catalog to ensure scalable ETL/ELT processes and data quality.
Data Engineer – Databricks
Build and optimize scalable data pipelines using Databricks, PySpark, and SQL, focusing on ETL/ELT, Delta Live Tables, and Unity Catalog.
Junior Data Engineer
Builds and maintains scalable data pipelines, writes SQL queries, and ensures data quality for AI/analytics solutions by integrating data from multiple sources into centralized repositories.
Junior Data Engineer
Builds and maintains scalable data pipelines to process raw data into actionable insights, collaborating with data scientists and engineers. Core techs: Python, SQL, ETL/ELT, and database architectures.
Senior Data Engineer
The Senior Data Engineer will design and maintain robust data pipelines and ETL processes using Azure Synapse Analytics and Databricks. They will collaborate with cross-functional teams to build scalable data infrastructure and ensure high data quality across the organization.
Senior Data Engineer
Designs, builds, and maintains cloud-based data pipelines and infrastructure for a FinTech wealth management platform, focusing on Airflow, Python/SQL, and Azure/Kubernetes. Ensures scalability, reliability, and operational efficiency for financial data workflows.
Senior Data Engineer
Builds and maintains scalable data pipelines for ingestion, transformation, and orchestration using Spark, Kafka, Airflow, and Informatica, deploying via CI/CD on containerized infrastructure.
Platform Architect
Designs and optimises an enterprise Azure Databricks data platform, focusing on Serverless compute, FinOps, and migrating legacy POSIT/RStudio workloads to modern data solutions.
Junior Data Engineer - Data Management AI & Analytics
Junior Data Engineer on a 1-year consultant contract in Lund, building ETL pipelines, reporting data models, and Power BI dashboards on the Microsoft Azure data platform (Data Lake, Data Factory, Databricks, SQL DW) using PySpark and Python.
Senior AWS Data Engineer - PySpark, ETL & Lakehouse
Design and maintain scalable AWS data pipelines using PySpark and Python, building ETL/ELT workflows with Glue and Step Functions.
Senior Hadoop & Spark Big Data Engineer
Designs and builds scalable big-data pipelines using Hadoop, Spark, and Python to process and warehouse large datasets efficiently.
Cloud Data Engineer (f/m/x)
Build and maintain cloud data pipelines using Python, SQL, and Azure tools like Databricks and Snowflake to process and transform large datasets.
MLOps Engineer / AI Data Engineer
Develops and maintains AI/ML pipelines, automates data workflows, and ensures scalable model deployment using cloud platforms and MLOps tools like MLflow/Kubeflow.