Data Engineer
The role involves working as a Data Engineer, utilizing various data processing tools to manage and transform data. You will be expected to be versatile and maintain a positive attitude while contributing to data engineering projects.
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The role involves working as a Data Engineer, utilizing various data processing tools to manage and transform data. You will be expected to be versatile and maintain a positive attitude while contributing to data engineering projects.
Design, build, and maintain scalable data pipelines while ensuring data quality and reliability across heterogeneous systems. Collaborate with product and engineering teams to translate business requirements into effective data solutions and mentor junior team members.
The role involves the development and optimization of data pipelines. You will work within Big Data ecosystems to ensure efficient data processing.
Design and build batch and near-real-time data pipelines using PySpark and Databricks within a medallion architecture. You will architect efficient Delta Lake schemas and collaborate with product teams to translate requirements into reliable data enrichment workflows.
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The role focuses on migrating data from Stratio to Databricks. The engineer will be responsible for managing data pipelines and integration processes.
Responsible for extracting, transforming, and analyzing data using SQL and Python to design and maintain ETL processes. The role involves developing dashboards and collaborating with stakeholders to translate business needs into analytical solutions.
Design, develop, and maintain scalable data pipelines to process and validate clinical and medical device data. Collaborate with cross-functional teams to ensure data quality and deliver analysis-ready datasets for statistical reporting.
You will design and maintain cloud infrastructure using Infrastructure as Code while managing data pipelines and platform services on GCP. The role involves supporting multi-cloud migrations, implementing automated governance, and leveraging AI tools to optimize platform resilience and performance.
The Project Manager will lead the technical supervision and management of high-voltage line projects for data centers, acting as the primary interface between clients, contractors, and utility companies. Responsibilities include managing project planning, technical reviews, risk mitigation, and ensuring compliance with design requirements throughout the construction and commissioning phases.
You will be responsible for developing, maintaining, and optimizing data ingestion and transformation processes. You will work with Big Data technologies to process large volumes of information within the BBVA environment.
You will design, develop, and evolve high-volume data pipelines within Big Data architectures. You will work as part of a Data Engineering team on strategic projects involving distributed processing.
Design and develop Airflow DAGs to integrate data from various internal and external sources. Manage the lifecycle of master data by developing processes for matching, deduplication, and quality control to ensure reliable golden records.
You will design, build, and evolve robust data pipelines and analytical models using tools like Airflow and dbt. Additionally, you will optimize data architecture performance and collaborate with business and BI teams to deliver sustainable data solutions.
Design and build reliable data pipelines, analytical models, and governed data products to support BI and AI-driven workflows. Lead technical standards for architecture, data quality, and observability while mentoring team members in AI-native engineering practices.
Design, build, and maintain scalable data pipelines to integrate data from multiple sources while ensuring data quality and security. Collaborate with cross-functional teams including data scientists and analysts to transform raw data into actionable business insights.
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Develop and maintain ETL processes using DataStage while optimizing data loads and performance. Provide support for incident resolution and participate in continuous improvement and evolutionary projects.
You will be responsible for the design, development, and evolution of data pipelines and Big Data solutions. You will work within a Data Engineering team to implement distributed architectures for strategic projects.
The selected candidate will join a Data Engineering team to design, develop, and evolve high-volume data processing pipelines. They will work on strategic banking projects utilizing Big Data technologies and distributed architectures.
Develop and orchestrate data solutions using pure Python scripts and complex SQL queries without visual ETL tools. Manage high-performance data ingestion and processing while ensuring robust error handling and data quality.
Participate in enterprise projects focused on knowledge graphs, ontologies, and semantic data structures. Perform data engineering tasks including ETL/ELT processes within cloud environments.
Design and develop custom data pipelines using Python and SQL without relying on visual ETL tools. Manage data ingestion, processing, and quality control while optimizing database performance for high-volume environments.
Develop and maintain data ingestion and transformation processes using Python and advanced SQL. Design robust ETL/ELT pipelines while ensuring data quality and optimizing performance for large datasets.
The role involves designing and maintaining data pipelines using Databricks. You will be responsible for implementing data solutions to support business requirements.
Design, develop, and maintain data solutions and pipelines using Snowflake and SQL. Create and evolve business intelligence models, dashboards, and reports using Power BI to meet business requirements.
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The role involves designing, building, and optimizing modern data pipelines and transformation models using Snowflake and dbt. You will collaborate with multidisciplinary teams to deliver scalable data solutions and ensure high-quality analytical outputs.
Design, develop, and maintain scalable data pipelines using Databricks and cloud-native technologies to support business intelligence and AI initiatives. Collaborate with cross-functional teams to build robust data models and ensure the reliability and performance of the analytics ecosystem.
Design, build, and maintain reliable batch and near-real-time data pipelines for critical payment and consumer data domains. Define and enforce data contracts, governance, and privacy-by-design principles while optimizing storage and compute performance.
Design and develop scalable cloud-based data architectures with a focus on performance, security, and reliability. Lead the implementation of data engineering best practices and mentor team members on ETL/ELT processes and platform capabilities.
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