Data Engineer

The role

Ideal candidates will have strong quantitative backgrounds and analytical discipline. While they will have some demonstrated ability to write code, they will not have learned programming languages for the sake of building their resume, but rather as a means to express their intellectual curiosity and analytical voice. Kin + Carta will provide a platform and training to help them reach their full potential.

Kin + Carta is looking to hire a Data Engineer to join our growing capabilities team. If you are innovative, passionate about data and AI technologies, and look to continually learn and enjoy sharing expertise, read on!

Due to the sensitive nature of this client project, this role requires you to be onsite in our Buenos Aires office 5 days a week.

Role responsibilities

• Analyze a collection of raw data sets to create meaningful impact to large enterprise clients while maintaining a high degree of scientific rigor and discipline.
• Engineer data pipelines and products to help stakeholders make and execute 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
• Conducting deep dive analysis and designing KPIs to help guide business decisions and measure success
• Engineering data infrastructure, software libraries, and APIs supporting BI and ML data pipelines
• Architecting cloud data platform components enabling the above
• Building and tracking project timelines, dependences, and risks
• Gathering stakeholder requirements and conducting technical due diligence toward designing pragmatic data-driven business solutions

Minimum qualifications

We want all new hires to succeed in their roles at Kin + Carta. That's why we've outlined the job requirements below. To be considered for this role, it's important that you meet all Minimum Qualifications. If you do not meet all of the Preferred Qualifications, we still encourage you to apply.

• Proven industry experience executing data engineering, analytics, and/or data science projects or Bachelors/Masters degree in quantitative studies including Engineering, Mathematics, Statistics, Computer Science or computation-intensive Sciences and Humanities.
• Proficiency (can execute data ingestion to insight) in programmatic languages such as SQL and Python.
• Proven experience using Databricks.

Preferred qualifications

• Proficiency in visualization/reporting tools such as Tableau and PowerBI or programmatic visualization library such as R ggplot2, Python matplotlib/seaborn/bokeh, 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 Kin + Carta

Kin + Carta is a global digital transformation consultancy committed to working alongside our clients to build a world that works better #ForEveryone. We build digital solutions that connect people, data and technology for some of the world’s most influential companies. As a , we are committed to , and our triple bottom line focus of people, the planet and profit.

Your benefits

Hard work deserves big rewards. Joining us means you’ll be able to enjoy a wide range of benefits aimed at mind, body, spirit and enhancing your career.

Affinity groups & culture committees Internal & external training opportunities Annual feedback cycles Award-winning workplace culture B Corp certified Prepaid health coverage for family Computer kit and home office equipment Monthly Wi-Fi reimbursement Coupon for online grocery shopping English classes offered once per week Additional week of vacation Quarterly salary adjustment