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Data Engineer

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Data Engineer

  • Location:


  • Job type:

    Temporary & Contract

  • Salary:

    £0.00 - £500 per day

  • Contact:

    Neo Crisp

  • Contact email:

  • Job ref:


  • Published:

    about 1 month ago

  • Duration:

    6 months

  • Expiry date:


  • Startdate:


  • Client:


Data Engineer

Location: London, UK

Contract Length: 6 months (with the option to extend)

Start Date: ASAP

Client: Global Investment Bank

Essential experience & job requirements:

  • Technical skills
        • SQL and RDBMs - Data Engineering
        • Python programming, including knowledge of pandas, numpy, Jupyter. Ability to write production ready, highly reliable, tuned (pythonic) numerical code
        • Dataiku / Alteryx / Altair or similar data science & data engineering products
        • Data visualisation tools such as Power BI, Tableau and Plotly
        • Excel / VBA programming / reverse engineering skills
  • Data Analysis, modelling and design skills
  • Strong analytical, reasoning and mathematical skills. Strong written and verbal communication skills.
  • Experience of working in agile teams and using tools such as JIRA / Azure DevOps
  • Software development industry best practice, including unit, integration and regression testing. Data testing. Build and deploy patterns.
  • Background in Trading / Financial services / Risk analytics / Insurance / eCommerce / Healthcare / Pharmaceuticals / FMCG domains or industries
  • Experience of using financial / timeseries market data
  • At least 5 years of total work experience in the industry

Desirable skills & experience:

  • Certified Agile practitioners/scrum masters with experience of implementing agile work practices
  • Certified Dataiku practitioners with experience of building models in Dataiku
  • Experience of optimising data pipelines and building scalable / high performance data solutions
  • Experience in ModelOps
  • Quantitative skills - Knowledge of statistics, probability theory and optimisation
  • Advanced python knowledge and data science knowledge - To include econometrics, OLS and Lasso, Logistic Regression, CART and Random Forests, SVMs, NNs, Radial Basis Functions and Kernel methods.
  • The scientific python stack including SciPy, scikit-learn, Statsmodels