Machine Learning Ops Data Engineer

Znalezione w: beBee jobs PL - 2 tygodni temu


Warszawa, Mazovia, Polska NatWest Polska Pełny etat

Join us as a Machine Learning Ops Data Engineer

  • This is an exciting opportunity to use your technical expertise to collaborate with colleagues and build effortless, digital first customer experiences
  • You'll be simplifying the bank through developing innovative data driven solutions, inspiring to be commercially successful through insight, and keeping our customers and the bank safe and secure
  • Participating actively in the data engineering community, you'll deliver opportunities to support our strategic direction while building your network across the bank

What you'll do

We'll look to you to drive value for the customer through modelling, sourcing and data transformation. You'll be working closely with core technology, architecture and Data Science teams to deliver strategic ML and AI Models, while driving Agile and DevOps adoption in the delivery of ML solutions.

We'll also expect you to be:

  • Delivering the automation of data engineering pipelines and ML processes to support the deployment of data science cases through the removal of manual stages
  • Anticipating the challenges associated with ML models in production and designing efficient, elegant solutions to these (e.g., retraining strategies, data drift and monitoring triggers)
  • Educating and embedding new data techniques into the business through role modelling, training and experiment design oversight
  • Supporting best practice for development of data and ML pipelines, as well as the creation of reusable code and data assets

The skills you'll need

To be successful in this role, you'll need to be an advanced level programmer and DevOps Engineer with a qualification in Computer Science, Software Engineering or a relevant quantitative discipline. You should have a proven track record in extracting value and features from large scale data, and ideally you will have experience of building machine learning pipelines. You'll also need a strong understanding of Data Science techniques. Finally, you should have worked with wider teams and the end customer for the deployment of solutions both on-prem and in the cloud.

You'll also demonstrate:

  • Experience of ETL technical design, automated data quality testing, QA and documentation, data warehousing, data modelling and data wrangling
  • Extensive experience using RDMS, ETL Pipelines, Phyton, Java, Scala, Hadoop and SQL
  • Experience in designing, building and deploying highly scalable and reliable data or ML pipelines using BigData techniques and tools (for example Airflow, Python, Redshift/Snowflake, Streamset)
  • Experience of shipping scalable data and ML solutions in the cloud (AWS, Azure, GCP)
  • Experience building Continuous Integration/Continuous Delivery pipelines and tools is a plus (Jenkins, TeamCity etc.)
  • Experience or strong understanding of DevOps & MLOps practice and principles
  • Good critical thinking and proven problem solving abilities

It would be ideal if you have experience of using Oracle, Unix scripting, working with NoSQL databases and working with pub/sub and event driven technologies like Kafka.


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