Position: Data Engineer I Zeki
Company: Global Talent
Experience required: 3y
English level: upper

This is becoming to grow in size and become more convoluted as technology changes in complexity. Currently, in the market, a simple and thorough solution doesn’t exist and does not serve intelligence communities worldwide well enough.

**Zeki** is developing a solution that carries a diverse data set, with key functionality to allow users to find talent based on varying criteria. Data will be collected and stored in a secure cloud solution.

Data will be parsed, labeled, and applied to machine learning predictors of success to identify common patterns across all data points. Lastly, a low code solution to quickly give users to search for talent based on different filter criteria, with some simple visualization.

❗️ Part-time: 20-30 hr/week (total: 60 hrs ~ 2-3 weeks) ❗️

🥊 **You will need:**
• At least 3 years of experience in data engineering and structuring databases
• Ability to manage large sets of data and organize it
• Experience in close collaboration with Machine Learning Engineers
• Experience using AWS tools and ability to set up structure and services from scratch
• Strong experience with REST APIs and data validation/washing
• Strong SQL and Python skills
• At least an Upper-Intermediate level of English
• Experience in data architecture using Amazon RDS
• Ability to transform data with rules consistent in retaining high quality data, such as duplication
• Experience with working with scattered data across many data sets.

🔥 **Nice-to-have skills:**
• Integrated AWS database into Low Code Bubble tool
• Automated ETL
• Managing Vendor Relationship
• Experience in writing technical documentation

🛠 **You will do:**
• Database design and structuring from scratch to fit for purpose for a low code solution
• Working with AWS and setting up instance
• Data Cleaning and Organizing
• Importing Data into S3
• Collecting data using REST APIs from another source as needed using SQL/Python
• Collaborating with the ML engineer
• Data Washing and Validation
• Managing relationship with the client and meeting expectations
• Working with the low-code developer to evaluate features that have or don’t have data

🪜 **Interview stages:**
• GT Interview
• Technical Interview