Ready for the new beginning?

Responsibilities:

Design scalable, efficient, and reusable Data Hub architectures to integrate structured and unstructured data from various sources.
Establish standards, best practices, and design patterns to ensure consistency across all implementations.
Guide and mentor other data engineers, offering technical direction and conducting code reviews.
Develop and optimize batch and real-time data processing workflows to enhance efficiency.
Implement data validation, anomaly detection, and automated monitoring to ensure high data reliability.
Lead efforts to automate data workflows, ensuring smooth deployment through DevOps pipelines.
Collaborate closely with architects, AI engineers, and business teams to align data solutions with organizational goals.
Ensure that data solutions comply with governance, security, and regulatory requirements.
Maintain and promote high-quality technical documentation for data models, pipelines, and best practices.

Qualifications:

Advanced expertise in Python and SQL for data engineering tasks.
Extensive experience with Azure Data Factory, ADLS, Azure SQL, and Synapse.
In-depth knowledge of Databricks (primary tool) and Apache Spark for scalable data processing.
Proven experience in designing enterprise-level data platforms, focusing on scalability, security, and efficiency.
Familiarity with real-time data processing tools, including Azure Stream Analytics, Event Hubs, or similar platforms.
Strong understanding of CI/CD, Terraform, Kubernetes/AKS, and Docker for infrastructure automation.
Experience ensuring data security, lineage tracking, and compliance with industry regulations.
Capability to guide and mentor data engineering teams, setting the technical direction.
Strong skills in creating clear and structured technical documentation, with the ability to communicate complex topics effectively.
Proficiency in English (both written and spoken), at a minimum B2 level.
Minimum 6 years of experience

Perks:

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1,000 - 1,310 PLN

B2B

Warszawa

Responsibilities:

Design scalable, efficient, and reusable Data Hub architectures to integrate structured and unstructured data from various sources.
Establish standards, best practices, and design patterns to ensure consistency across all implementations.
Guide and mentor other data engineers, offering technical direction and conducting code reviews.
Develop and optimize batch and real-time data processing workflows to enhance efficiency.
Implement data validation, anomaly detection, and automated monitoring to ensure high data reliability.
Lead efforts to automate data workflows, ensuring smooth deployment through DevOps pipelines.
Collaborate closely with architects, AI engineers, and business teams to align data solutions with organizational goals.
Ensure that data solutions comply with governance, security, and regulatory requirements.
Maintain and promote high-quality technical documentation for data models, pipelines, and best practices.

Qualifications:

Advanced expertise in Python and SQL for data engineering tasks.
Extensive experience with Azure Data Factory, ADLS, Azure SQL, and Synapse.
In-depth knowledge of Databricks (primary tool) and Apache Spark for scalable data processing.
Proven experience in designing enterprise-level data platforms, focusing on scalability, security, and efficiency.
Familiarity with real-time data processing tools, including Azure Stream Analytics, Event Hubs, or similar platforms.
Strong understanding of CI/CD, Terraform, Kubernetes/AKS, and Docker for infrastructure automation.
Experience ensuring data security, lineage tracking, and compliance with industry regulations.
Capability to guide and mentor data engineering teams, setting the technical direction.
Strong skills in creating clear and structured technical documentation, with the ability to communicate complex topics effectively.
Proficiency in English (both written and spoken), at a minimum B2 level.
Minimum 6 years of experience

Perks: