Ebury
FinTech
DataAnalyticsEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“Data Analytics Engineer at Ebury. Skills: Data Platform, Analytics Engineering, Data modelling, ETL/ELT, SQL, Python, Google Cloud Platform, dbt, Airflow. Build ETLs and data pipelines to serve data in our platform. Provide clean, transformed data ready for analysis and used by our BI tool”
What You'll Achieve.
Drive decision-making; Spend time on high-value analysis rather than running data extracts; Ensure that data-related processes are efficient, secure, and adhere to industry standards
Industry & Context.
Problem-solving
What They're Looking For.
Must Have
2+ years of data/analytics engineering experience building, maintaining & optimising data pipelines & ETL processes on big data environments, Proficiency in SQL, Mid - Level experience with Python, Experience with software engineering practices in data, Attention to detail and commitment to data quality, Advanced in English
Nice to Have
Experience with our modern data stack tools (a plus!), Familiarity with dimensional modelling/data warehousing concepts, Spanish (a plus)
What You'll Do.
Build ETLs and data pipelines to serve data in our platform
transformed data ready for analysis and used by our BI tool
Develop department and project-specific data models and serve these to teams across the company to drive decision-making
Automate end solutions so we can all spend time on high-value analysis rather than running data extracts
Gather requirements from the business and define an action plan to execute them
Work on projects end to end
from definition to delivery
Serve hands-on delivery of data models using solid software engineering practices
Manage overall pipeline orchestration using Airflow
deploy and maintain ELT/ETL data pipelines from a variety of data sources
Contribute to and promote best practices for query development
Help model and maintain the Data Platform
Promoting best practices to ensure that data-related processes are efficient
and adhere to industry standards
How You'll Work.
Team & Collaboration
Collaborate with stakeholders to understand the requirements, advising on and influencing the solutions, and taking ownership of data implementation and delivery; Collaborate with team members to reinforce best practices across the platform, encouraging a shared commitment to quality; Data Scientists, Data Engineers, Analytics Engineers and Data Analysts work collaboratively
Communication Scope
Communication
Process & Methodology
Work on projects end to end, from definition to delivery, Jira as project management tool
Full Job Description
Ebury helps ambitious businesses unlock global growth, and we take the same approach with our people. We encourage innovation and movement, collaboration and problem-solving, and foster an environment where everyone can feel they belong, are valued, supported and empowered to succeed. If you’re a collaborator who wants to help transform how businesses operate globally, get in touch - we’d love to discuss how Ebury can accelerate your career so you can shape the future. Data Analytics Engineer Location: São Paulo (4 days on-site, 1 day remote) Ebury is a FinTech success story, positioned among the fastest-growing international companies in its sector. Headquartered in London, we have more than 1,000 staff covering over 50 nationalities (and counting!) working across more than 27 offices worldwide and serving more than 45,000 clients every day. About our team: Ebury’s strategic growth plan would not be possible without our Data team and we are seeking a Analytics Engineer to join our Analytics Engineering team! Our data mission is to develop and maintain Ebury’s Data Platform and serve it to the whole company, where Data Scientists, Data Engineers, Analytics Engineers and Data Analysts work collaboratively to: Build ETLs and data pipelines to serve data in our platform Provide clean, transformed data ready for analysis and used by our BI tool Develop department and project-specific data models and serve these to teams across the company to drive decision-making Automate end solutions so we can all spend time on high-value analysis rather than running data extracts Collaborate with stakeholders to understand the requirements, advising on and influencing the solutions, and taking ownership of data implementation and delivery About our technology and Data stack: Google Cloud Platform as our main Cloud provider Apache Airflow as orchestration tool Cloud Build as CICD dbt as data modelling and warehousing Looker and Looker Studio as Business Intelligence/dashboarding Githu
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