資料工程：12,244 個職缺
瀏覽與 資料工程 相關的開放職缺。
Senior Data Engineer
Design and build scalable cloud-native data platforms from greenfield to production, including near-real-time event-driven pipelines, Data Lake/Lakehouse architectures, and AI-ready infrastructure. Core stack: Python, SQL, Apache Spark/PySpark, Databricks, Kafka, Airflow, Azure/AWS/GCP, Terraform.
Data Engineer – Microsoft Fabric / Azure
Consultant Data Engineer role building scalable data solutions on Microsoft Azure and Microsoft Fabric across varied client projects in Stockholm. Day-to-day work centers on SQL, data pipelines, data modeling/transformation, and integrating data sources.
LEAD DATA STEWARD
Lead Data Steward at Scandinavian Airlines (SAS) establishing and driving enterprise data governance — defining policies for data quality, metadata, lineage, and access management across customer, loyalty, commercial, and operations domains to enable trusted AI and analytics.
Data Engineer (Middle / Strong Middle)
Data Engineer at DareBay, a UGC marketplace platform connecting brands with content creators, owning and scaling the data infrastructure. Builds batch/streaming ETL/ELT pipelines using Python, advanced SQL, Spark/Flink, Airflow, dbt, Snowflake/BigQuery/Redshift, Kafka, Docker, and Kubernetes.
AI Data Engineer - Early Career
Early-career AI Data Engineer at DarioHealth building dashboards, KPIs, and ETL/ELT pipelines while contributing to AI-enabled automation. Day-to-day work involves writing Python and SQL, maintaining data models and warehouses, and ensuring data quality and reliability.
Data Engineer (команда RecSys)
Build and maintain batch ETL pipelines (Airflow) and real-time stream processing (Spark Streaming, Kafka) for analytical data marts and ML models, and grow the feature store. Stack: Python, SQL, Airflow, Spark, Kafka, ClickHouse, MongoDB.
Senior Data Engineer (Отдел разработки DWH и Data инженерии)
Senior Data Engineer at a bank building a Lakehouse platform on Spark + Iceberg, developing ETL/ELT pipelines in Apache Airflow, and writing high-performance analytical queries with Trino for a hybrid team in Almaty.
Senior Data Engineer
Designs and builds data pipelines and models in Snowflake for BlackLine's AI-powered Invoice-to-Cash SaaS platform, then surfaces trusted insights through Power BI. Works hands-on with SQL, Python, dbt/Airflow, and AI development tools (Cursor, Claude) within a BI & Analytics Engineering team.
Data Engineer
Data Engineer at Stratosphere working on a large bank project, building and maintaining ETL/ELT pipelines, data marts, and ML feature stores using Python, SQL, Greenplum, Apache Airflow, Apache Spark, and Trino.
Data Engineer - Senior 2
Senior data engineer at Cummins leading the design, development, and maintenance of a cloud-based data and analytics platform. Builds scalable data pipelines, data products, and AI/ML-ready datasets using Spark, Hadoop, SQL, Kafka, and modern ETL/ELT tools across Supply Chain, Finance, and Product domains.
Data Engineer - Senior 1
Senior Data Engineer at Cummins leading design and development of enterprise data pipelines and analytics platforms using Spark, Hadoop, Kafka, cloud data warehouses, and ETL/ELT tools, supporting analytics, AI/ML, and GenAI use cases across Supply Chain, Finance, and other domains.
Data Engineer 9
Data Engineer building and maintaining enterprise data pipelines, ETL/ELT transformations, and curated datasets across Supply Chain, Quality, Finance, and Product Lifecycle domains to power analytics, automation, and GenAI use cases using cloud big-data platforms.
Data Engineer 5
Data Engineer at Cummins building and maintaining enterprise-scale data pipelines, ETL/ELT transformations, and governed data products on cloud and Big Data platforms to support analytics, AI/ML, and GenAI use cases across Supply Chain, Quality, Finance, and Product Lifecycle domains.
Data Engineer 12
Data Engineer at Cummins building enterprise data products, pipelines, and curated datasets that support analytics, automation, and GenAI use cases across Supply Chain, Quality, Finance, and Product Lifecycle using Spark, Kafka, cloud platforms, SQL, and modern ETL/ELT tooling.