BI Data Engineer

Budapest - Hungary

Monthly Salary: Not Disclosed

Posted on: 4 hours ago

Vacancies: 1 Vacancy

Job Summary

Were looking for an experienced and highly motivated BI Data Engineer for one of our clients a global data analytics and market intelligence company. Youll will build scalable pipelines robust data models and reliable infrastructure that enable teams across the organisation to access accurate high-quality data.

and nbsp;

Responsibilities:

Architect and develop scalable resilient high-quality data solutions to support analytics reporting and machine learning workloads.
Design implement and maintain reliable ETL/ELT pipelines that integrate cleanse and consolidate data from diverse internal and external sources including APIs and third-party systems.
Ensure data is accurately ingested transformed stored and made accessible through robust testing frameworks and clearly defined business rules.
Translate business requirements into scalable data models and transformation logic working with large multidimensional datasets to surface insights trends and opportunities.
Build and maintain the BI reporting codebase ensuring high standards of data integrity availability and performance across the organization.
Create and maintain backend infrastructure that supports analytics platforms dashboards and machine learning pipelines.
Develop and execute unit and integration tests for data pipelines and transformation scripts to ensure reliability and consistency.
Proactively identify inefficiencies and bottlenecks in data flows and propose scalable forward-looking solutions.
Partner closely with product managers engineers commercial teams and business stakeholders to understand strategy and data needs aligning delivery with the BI roadmap.
Contribute to team growth through code reviews design discussions and knowledge sharing while staying current with industry best practices.
Take ownership of the quality integrity and consistency of the BI data codebase and associated datasets.
Ensure ETL efficiency and data availability to meet stakeholder requirements and business SLAs.
Adhere to engineering best practices including documentation version control and maintainable code standards.
Support and deliver against team OKRs and KPIs contributing to overall team and business success.

Requirements:

2-4 years of back-end and/or data engineering experience delivering production-grade data solutions.
Demonstrated experience working in a global or international environment collaborating across regions and time zones.
Significant experience working with Python.
Experience with SQL and NoSQL databases for OLTP and OLAP usages.
Experience ingesting and processing data from external APIs.
Experience with GCS (Google Cloud Services) and modern data warehousing (ideally BigQuery).
Familiarity with BI data architecture and version control tools such as GitHub or GitLab.
Understanding of performance optimization.
Strong understanding of the business logic behind datasets with an ability to ensure trust and data quality.
Analytical detail-oriented and committed to building reliable scalable solutions.
Comfortable working with unstructured or imperfect data and transforming it into actionable insights.
A collaborative team player who thrives in a global multicultural environment.
Strong communicator who can translate complexity into clarity for non-technical stakeholders.

Benefits:

Competitive salary
Opportunity to be part of a fast-growing company with a strong global presence
Home-office
Supportive diverse and professional work environment
Opportunities for professional development and career growth
Exposure to international projects and cross-functional collaboration
Stable company background with long-term career prospects
Dynamic and collaborative team culture.

and nbsp;

Responsibilities:

Architect and develop scalable resilient high-quality data solutions to support analytics reporting and machine learning workloads.
Design implement and maintain reliable ETL/ELT pipelines that integrate cleanse and consolidate data from diverse internal and external sources including APIs and third-party systems.
Ensure data is accurately ingested transformed stored and made accessible through robust testing frameworks and clearly defined business rules.
Translate business requirements into scalable data models and transformation logic working with large multidimensional datasets to surface insights trends and opportunities.
Build and maintain the BI reporting codebase ensuring high standards of data integrity availability and performance across the organization.
Create and maintain backend infrastructure that supports analytics platforms dashboards and machine learning pipelines.
Develop and execute unit and integration tests for data pipelines and transformation scripts to ensure reliability and consistency.
Proactively identify inefficiencies and bottlenecks in data flows and propose scalable forward-looking solutions.
Partner closely with product managers engineers commercial teams and business stakeholders to understand strategy and data needs aligning delivery with the BI roadmap.
Contribute to team growth through code reviews design discussions and knowledge sharing while staying current with industry best practices.
Take ownership of the quality integrity and consistency of the BI data codebase and associated datasets.
Ensure ETL efficiency and data availability to meet stakeholder requirements and business SLAs.
Adhere to engineering best practices including documentation version control and maintainable code standards.
Support and deliver against team OKRs and KPIs contributing to overall team and business success.

Requirements:

2-4 years of back-end and/or data engineering experience delivering production-grade data solutions.
Demonstrated experience working in a global or international environment collaborating across regions and time zones.
Significant experience working with Python.
Experience with SQL and NoSQL databases for OLTP and OLAP usages.
Experience ingesting and processing data from external APIs.
Experience with GCS (Google Cloud Services) and modern data warehousing (ideally BigQuery).
Familiarity with BI data architecture and version control tools such as GitHub or GitLab.
Understanding of performance optimization.
Strong understanding of the business logic behind datasets with an ability to ensure trust and data quality.
Analytical detail-oriented and committed to building reliable scalable solutions.
Comfortable working with unstructured or imperfect data and transforming it into actionable insights.
A collaborative team player who thrives in a global multicultural environment.
Strong communicator who can translate complexity into clarity for non-technical stakeholders.

Benefits: