About the role
Job Summary
We are a global workforce committed to building a world that works better for everyone. Our Kin is at the heart of everything we do, and we're looking for talented individuals to join our growing capabilities team.
About the Role
The Business Intelligence Analyst – Data Engineer will analyze large datasets, design data pipelines, and communicate findings in an intuitive and visually compelling way. The ideal candidate will have a strong quantitative background, analytical discipline, and experience with data engineering, analytics, and data science projects.
Responsibilities
Analyze raw data sets to create meaningful impact for large enterprise clients, maintaining scientific rigor and discipline.
Engineer data pipelines and products to help stakeholders make data-driven decisions.
Communicate analytical findings in an intuitive and visually compelling way, creating highly visual and interactive dashboards via Tableau, PowerBI, or custom web applications.
Conduct deep dive analysis and design KPIs to guide business decisions and measure success.
Engineer data infrastructure, software libraries, and APIs supporting BI and ML data pipelines.
Architect cloud data platform components enabling the above.
Build and track project timelines, dependencies, and risks.
Gather stakeholder requirements and conduct technical due diligence toward designing pragmatic data-driven business solutions.
Requirements
Proven industry experience executing data engineering, analytics, and/or data science projects, or Bachelor's/Master's degree in quantitative studies, including Engineering, Mathematics, Statistics, Computer Science, or computation-intensive Sciences and Humanities.
Proficiency in executing data ingestion to insight in programmatic languages such as SQL, Python, and R.
Preferred Qualifications
Proficiency in visualization reporting tools, such as Tableau and PowerBI, or programmatic visualization libraries, such as R ggplot2, Python matplotlib seaborn bokeh, and Javascript D3.
Proficiency in big data environments and tools, such as Spark, Hive, Impala, Pig, etc.
Proficiency with cloud architecture components, AWS, Azure, Google.
Proficiency with data pipeline software, such as Airflow, Luigi, or Prefect.
Ability to turn raw data and ambiguous business questions into distilled findings and recommendations for action.
Experience with statistical and machine learning libraries, along with the ability to apply them appropriately to business problems.
Experience leading and managing technical data analytics, machine learning projects.
About Us
You'll join an international network of data professionals within our organization. We support continuous development through our dedicated Academy. If you're looking to push the boundaries of innovation and creativity in a culture that values freedom and responsibility, we encourage you to apply.
