Data Engineer Job at Prunedge

    Company: Prunedge

    Number of Slot: 1

    Specialization: Information Technology / ICT

    Work Level: Experienced (Non-Manager)

    Job Type: Full-Time

    Experience: 3-5 years

    Salary Range:

    Minimum Qualification:

    Location(s): Lagos

Prunedge is a technology company that develops innovative solutions that improve efficiency of processes, livelihood of people and aid decision making within organizations. We actively work to put a smile on people’s faces by showing them the immense benefits of technology and delivering impact-focused and outstanding solutions and inventions.

We are recruiting to fill the position below:

Job Position: Data Engineer

Job Location: Lagos

Job Brief

  • We are looking for an experienced data engineer to join our team. You will use various methods to transform raw data into useful data systems. For example, you’ll create algorithms and conduct statistical analysis. Overall, you’ll strive for efficiency by aligning data systems with business goals.
  • To succeed in this data engineering position, you should have strong analytical skills and the ability to combine data from different sources.
  • Data engineer skills also include familiarity with several programming languages and knowledge of learning machine methods.
  • If you are detail-oriented, with excellent organizational skills and experience in this field, we’d like to hear from you.


  • University degree (or equivalent) in quantitative field: Statistics, Mathematics, Computer Science, Electrical Engineering, Engineering Statistics, Systems Engineering, or relevant fields
  • Minimum of 3 years professional experience in Data Engineering
  • Experience in developing machine learning models and applying advanced analytics solutions to solve complex business problems
  • Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas
  • Experience with modern deep learning frameworks: RLlib, PyTorch, TensorFlow, etc.
  • Experience using statistical computer languages (R, Python, SLQ, Julia, MatLab etc.) to manipulate data and draw insights from large data sets.
  • Experience with distributed data/computing tools: Ray, Map/Reduce, Spark, etc.
  • Experience with NoSQL databases, such as MongoDB, Cassandra, HBase
  • Experience with unsupervised and supervised machine learning techniques and methods
  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
  • Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications
  • Experience working with and visualizing large-scale (e.g., terabyte and petabyte) unstructured and structured data sets and databases
  • Experience with the design or use of production pipelines for online learning and reinforcement learning
  • Experience working with and creating data architectures
  • Proven DevOps CI/CD, QA Automation experience
  • Experience performing good unit testing and peer reviews before delivering code to QA
  • Proficiency with SQL programming
  • Experience working with statistical software packages including: SAS, SPSS Modeler, R, WEKA, or equivalent
  • Excellent analytical and multitasking skills
  • Self-motivated and creative problem-solvers who love to challenge themselves
  • An ability to perform well in a fast-paced environment
  • Ability to select hardware to run an ML model with the required latency
  • Proficient understanding of code versioning tools, such as Bitbucket, Git, Mercurial, SVN etc.


  • Understanding business objectives and developing models that help to achieve them, along with metrics to track progress
  • Analyzing ML algorithms and ranking them by their success probability
  • Exploring and visualizing data
  • Identifying differences in data distribution that could affect performance
  • Verifying data quality, and/or ensuring it via data cleansing
  • Supervising the data acquisition process
  • Finding available datasets online
  • Defining data augmentation pipelines
  • Training models and tuning hyperparameters
  • Analyzing the errors of the model and designing strategies to overcome them
  • Set up and manage AI development and production infrastructure
  • Build data ingest and data transformation infrastructure
  • Build and convert AI/machine learning models into APIs so that other applications can access them
  • Test and deploy AI models into production
  • Help product managers and business stakeholders understand results of AI/ML models
  • Develop MVPs based on AI/machine learning
  • Use AI to empower the company with novel capabilities
  • Keep current of latest AI research relevant to our business domain.
  • Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.