Link Group

Azure Data Engineer with Databricks

120 - 130 PLN/ godz./ godz.B2BB2B (netto)

SeniorZdalnieFull-time·B2B

#461386·Dodano 16 dni temu·3

Źródło: nofluffjobs.com

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Tech Stack / Keywords

SQL PySparkScripting languageData modelingSnowflakeData LakeDatabricksAzure Data FactoryAzure Cloud AWS ETL UNITYPower BIPython MLflowCloudAPIAzure DevOpsCI/CDDesign PatternsDevOps GCP

Podsumowanie oferty

Develop and implement scalable data processing solutions on Azure, transforming and cleaning large datasets. Design and maintain data pipelines using Databricks and Azure Data Factory, working across data platform project stages with cross-functional teams.

Wymagania

Hands-on experience implementing solutions with data lakes and pipelines using Databricks and Azure Data Factory.
Proven experience designing and implementing solutions in the Azure cloud.
Proficiency in SQL, PySpark, data modeling (3NF, star/snowflake schemas), and ELT/ETL design patterns; knowledge of popular ETL tools, including Azure Data Factory.
Experience with Python or similar scripting languages for data transformation, orchestration, and automation.
Familiarity with Databricks features, including Unity Catalog, MLflow, Jobs, and Pipelines.
Understanding of cloud data architecture principles, including data Lakehouse concepts, Medallion architecture, API integration, and event-driven architecture.
Experience with CI/CD and DevOps practices for data workflows, including Git and Azure DevOps, and analytics integration such as Power BI.
Excellent communication and client-facing skills, including the ability to explain technical concepts clearly to team members and stakeholders.
Strong collaboration skills across multidisciplinary teams.

Nice to have:

Knowledge of AWS and GCP clouds.

Obowiązki

Develop and implement scalable data processing solutions on Azure, transforming and cleaning large structured and unstructured datasets.
Design, build, and maintain data pipelines integrating data from databases, APIs, and streaming platforms.
Collaborate with data engineers, data scientists, and cloud architects to deliver end-to-end data platform solutions.
Engage at critical project development stages with business analysts, source system experts, data architects, engineers, and product owners.
Communicate and document technical work, considering performance and cost in design decisions.

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