We are seeking a talented Data Engineer to join our client's esteemed Global Identity and Fraud Analytics team. This role offers a dynamic environment, working on diverse and challenging projects across various industries including Financial Services, Telecommunications, eCommerce, Healthcare, Insurance, and Government.

Key Responsibilities:

• Work closely with the data scientist team to migrate analytical data and projects to the GCP environment, ensuring seamless project transition.
• Prepare and build data and analytical automation pipelines for self-serving machine learning projects, including data gathering, integration, consolidation, cleansing, and structuring.
• Design and code analysis scripts to run on GCP using BigQuery/Python/Scala, leveraging multiple Core data sources.

Qualifications:

• 3-5 years of professional data engineering or data wrangling experience in:
  • Working with Hadoop-based or Cloud-based big data management environments.
  • Bash scripting or similar experience for data movement and ETL.
  • Big data queries in Hive/Impala/Pig/BigQuery (Sufficient in BigQuery API libraries for data prep automation is a plus).
  • Advanced Python programming (Scala is a plus) with strong coding experience. Proficiency in data studio, Big Table, GitHub working experience (Cloud composer and Data flow are pluses).
  • Basic GCP certification is a plus.
  • Knowledge of Kubernetes is a plus (or other types of GCP native tools for container-orchestration).
  • Basic knowledge in machine learning (ensemble models, unsupervised models) with experience using Tensorflow and PyTorch is a plus.
  • Basic knowledge in graph mining and graph data model is a plus.
  • Understanding of best practices for data management, maintenance, and reporting.

Additional Qualifications:

• 3+ years of professional experience as a data engineer.
• 3+ years working with Python and SQL.
• Experience with state-of-the-art machine learning algorithms (deep neural networks, support vector machines, boosting algorithms, random forest, etc.) preferred.
• Experience conducting advanced feature engineering and data dimension reduction in a Big Data environment is preferred.
• Strong SQL skills in a Big Data environment (Hive/Impala, etc.) are a plus.