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Senior Data Engineer at Moniepoint builds and maintains large-scale data pipelines and optimizes the data platform for speed, scalability, and cost, supporting teams across a payments and banking platform. Core stack: SQL, Python, cloud platforms (Google Cloud/AWS/Azure), and Git.
QA Data Engineer validating data warehouse pipelines, ETL processes and dbt transformations using Snowflake, AWS, SQL and related tools, for a Nordic financial services client. Hybrid role (2-3 office days/week) from Gdańsk/Gdynia, Warszawa or Łódź, paid 1000-1100 PLN/day on B2B.
Senior Data Engineer building a multicloud (Azure + GCP) data ecosystem on Databricks and Spark that powers production LLM/RAG systems — including document chunking, embeddings, vector search, and Airflow-orchestrated pipelines. Fully remote across Poland, with optional use of a Warsaw office.
Lead Data Engineer owning enua Pharma's Databricks data platform at a fast-growing medical cannabis producer in Cologne: designing the company-wide data model, integrating ERP/CRM and market data, building LLM-based internal tools, and setting engineering standards — hands-on with Python, SQL, and Databricks, supported by one junior engineer.
You’ll help clients build data-governance frameworks, set standards for data quality and metadata, and embed compliance processes so their data becomes a trusted, strategic asset.
Senior Data Engineer at PUR, a climate-focused B Corp, building and maintaining scalable Python ETL/ELT pipelines, data models, and data quality controls that power analytics and reporting. The stack centers on workflow orchestration (Airflow, Prefect, or Dagster), a cloud data platform (preferably Azure), Terraform, and Git-based engineering practices.
Data Engineer for banking projects via outstaffing company Decart IT: design and run production ETL/ELT data pipelines, orchestrate with Apache Airflow, build high-load Spark/PySpark and Kafka streaming jobs, and work with industrial databases (PostgreSQL, Greenplum, Oracle, ClickHouse, MS SQL) and data warehouses. Remote anywhere within Russia.
Build and own parts of F-Secure's Enterprise Data Platform in Helsinki: ingest data from business systems, model it in Data Vault 2.0, and build Power BI semantic models and secure APIs feeding reporting, analytics, and AI agents. Core stack: SQL, Agile Data Engine, Databricks/AWS (migrating from Snowflake/Azure), REST/OAuth, with daily use of AI coding agents like Claude Code.
Principal-level data engineer who builds and enhances Databricks pipelines on Azure for healthcare data (claims, encounters, FHIR, EHR) using Python, PySpark and SQL. Works with product teams, data engineers and clinical leaders to deliver trusted, high-quality data products.
A 12-month fixed-term Data Governance Analyst role at Macmillan Cancer Support, joining a small collaborative team to strengthen how the charity's data is understood, managed, and governed. Hybrid working from home with roughly 1 office day per month (London, Shipley, or Glasgow), salary £43,500–£47,500.
Mid-level (3-5 yrs) data engineer who designs, builds and troubleshoots production ETL/ELT pipelines, data warehouses and models day to day. Core stack: Python (Pandas/PySpark), advanced SQL, Airflow/dbt, Snowflake/BigQuery/Redshift/Databricks and AWS/Azure/GCP, with support for AI/ML data workloads.
Senior Data Engineer building an AI-powered, governed analytics 'Intelligence Layer' on Databricks for what appears to be a travel/aviation client. Day to day: designing data pipelines and curated datasets in Unity Catalog, defining Metric Views, tuning Genie Agents, and building multi-agent AI orchestration with MLflow tracing, using Python and SQL.
A Data Engineer at Central Adelaide Local Health Network (CALHN) designs, builds and optimises modern data platforms and pipelines, integrating complex healthcare data from EMR systems, clinical registries and external providers. Core stack includes Snowflake, Denodo, Databricks, Azure Data Factory, FHIR and HL7.
Senior Data Engineer on a high-load network-traffic post-analysis system at a Russian telecom-infrastructure vendor (Rostelecom group). Day to day: designing batch/streaming pipelines, leading the Hadoop → S3/Iceberg migration, optimizing Spark and Trino for near-real-time processing. Core stack: Spark, Kafka, Python, Iceberg, Trino.
Leads technical delivery of data migration and engineering initiatives: designing scalable data pipelines in GCP, guiding an engineering pod, and promoting development standards within an Agile process. Core stack includes PySpark/Scala, Spark, Hadoop ecosystem tools, Airflow, Python, SQL, and GCP DevOps tooling.
Lab49/ION is hiring a Mexico City-based Senior Data Engineer to design, build, and operate scalable data platforms for market and credit counterparty risk in financial services. Day-to-day work centers on Python, AWS, Airflow, and Snowflake, plus CI/CD automation, monitoring, and stakeholder collaboration in an agile team.
Builds the company's data foundation: high-volume web scraping systems, structured datasets (including SEC Form D filings), and LLM-driven ETL pipelines. Core stack includes Scrapy/Playwright, Airflow, BigQuery/PostgreSQL/Supabase, Docker, FastAPI, and GCP.
Senior Data Engineer designing, building, and maintaining scalable data pipelines for enterprise analytics platforms. Day-to-day work centers on ELT/ETL development with Snowflake, dbt, Airflow, and AWS, plus data quality monitoring, query optimization, and collaboration with analytics and BI teams.
An Azure Data Engineer builds batch ingestion pipelines from EMR, claims, and CRM sources into a Medallion (Bronze/Silver/Gold) architecture for a health-tech startup, using Azure Data Factory, SQL, PySpark/Databricks, Synapse, Key Vault, and Purview, with a focus on data quality, lineage, and governance. Full-time, hybrid in Guadalajara, working U.S. Eastern Time hours.
Data Engineer at PALO IT, a global technology consultancy, who designs, builds, and maintains scalable ETL/ELT pipelines, data lakes, and warehouses in cloud environments (AWS/GCP/Azure) using Python, SQL, Spark, Hadoop, and Airflow, collaborating with data scientists and business teams.
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