Job Description

Bring in industry best-practices around creating and maintaining robust data pipelines for complex data projects with / without AI component: o programmatically retrieve (unstructured mostly) data from several static and real-time sources (incl. web scraping, API use) o Structure this data into a structured format o Harmonize the data, into a common format and store it in a dedicated database. o Schedule the different jobs into a dedicated pipeline o rendering results through dynamic interfaces incl. web / mobile / dashboard with ability to log usage and granular user feedbacks o performance tuning and optimal implementation of complex Python scripts, SQL, • Industrialize ML / DL solutions and deploy and manage production services; proactively handle data issues arising on live apps • Perform ETL on large and complex datasets for AI applications - work closely with data scientists on performance optimization of large-scale ML/DL model finetuning • Build data tools to facilitate fast data cleaning and statistical analysis • Build and ensure data architecture is secure and compliant • Resolve issues escalated from Business and Functional areas on data quality, accuracy, and availability • Work closely with APAC IT Transformation and coordinate with a fully decentralized team across different locations in APAC and global HQ (Paris). You should be • Expert in structured and unstructured data in traditional and Big data environments – Oracle / SQLserver, MongoDB, Hive / Pig, BigQuery and Spark • Have excellent knowledge of Python programming both in traditional and distributed models (PySpark) • Expert in shell scripting and writing schedulers • Hands-on experience with Cloud - deploying complex data solutions in hybrid cloud / on-premise environment both for data extraction / storage and computation • Experience working on industry standard services like Message Queue, Redis, Elastic Search, Kafka, or Spark Streaming • Well versed with DevOps best practices like containerization, CICD pipeline (Jenkins and Maven) • Hands-on experience in deploying production apps using large volumes of data with state-of-the-art technologies like Dockers, Kubernetes and Kafka C2 - Internal Natixis • Strong knowledge of data security best practices • 10+ years’ experience in data engineering role • Graduate from a Tier-1 university • Knowledge of finance and experience in handling company annual reports would be greatly appreciated • And most importantly, you must be a passionate coder who really cares about building apps that can help us do things better, smarter and faster

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