Data Engineer, Demyst

• Bachelor's in Computer Science, Data Science, Engineering or similar technical discipline (or commensurate work experience); Master's degree preferred
• 1-3 years of Python programming (with Pandas experience)
• Experience with CSV, JSON, parquet, and other common formats
• Data cleaning and structuring (ETL experience)
• Knowledge of API (REST and SOAP), HTTP protocols, API Security and best practices
• Experience with SQL, Git, and Airflow
• Strong written and oral communication skills
• Excellent attention to detail
• Ability to learn and adapt quickly

• Collaborate with internal project managers, sales directors, account managers, and clients’ stakeholders to identify requirements and build external data-driven solutions
• Perform data appends, extracts, and analyses to deliver curated datasets and insights to clients to help achieve their business objectives
• Understand and keep current with external data landscapes such as consumer, business, and property data
• Engage in projects involving entity detection, record linking, and data modelling projects
• Design scalable code blocks using Demyst’s APIs/SDKs that can be leveraged across production projects
• Govern releases, change management and maintenance of production solutions in close coordination with clients' IT teams

• Work with the largest consumer and business external data market in an emerging industry that is fueling AI globally
• Have an impact in a scaling but small team offering real autonomy and responsibility for client outcomes
• Stretch yourself to help define and support something entirely new that will impact billions
• Work within a strong, tight-knit team of subject matter experts
• Small enough where you matter, big enough to have the support to deliver what you promise
• Distributed working team and culture, recognition of outcomes and merit, not presenteeism
• Generous benefits

Total funding: $24.5m

The power of external data is a hot topic, particularly for financial institutions and insurers who must stay compliant as they roll out new products. However, with the data landscape becoming increasingly fragmented, deploying external data at scale has become a significant challenge.