Data Science Platform Engineer - Business Intelligence

Responsibilities

• Collaborate closely with Product Managers, Data Scientists, Data Engineers, and Platform Engineers
• Design and build foundational, reusable data layers to support data science products
• Develop and maintain data science model and algorithm pipelines
• Create internal platforms, tools, and modules to accelerate data science delivery
• Design and manage cloud infrastructure for large-scale experimentation and production workloads
• Follow engineering, product, and data management best practices
• Lead standardization efforts and core platform development initiatives
• Develop generative AI (LLM-based) tools to enhance data science workflows

• Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related field
• Solid understanding of data science fundamentals
• Strong expertise in distributed data processing tools (e.g., Trino, Spark
  
  SQL, PySpark, Ray)
• Experience with distributed file systems (e.g., HDFS, S3, Google Cloud Storage)
• Familiarity with metadata and catalog services (e.g., Hive Metastore, Unity Catalog)
• Understanding of data lake and lakehouse architectures
• Strong knowledge of data management principles
• Proficiency in object-oriented and functional programming (preferably Python and Go; Scala/Java is a plus)
• Strong experience in UNIX/Linux environments
• Solid understanding of containerization and Docker
• Familiarity with SDLC practices (version control, TDD, documentation)
• Understanding of algorithm complexity (Big‑O notation)
• Experience with orchestration tools (e.g., Apache Airflow, Temporal)
• Strong knowledge of Kubernetes (preferably GKE)
• Experience with cloud platforms (preferably GCP)
• Familiarity with Git Ops practices (CI/CD, Git Lab CI, Git Hub Actions)
• Experience building generative AI/LLM-based tools is a plus
• Understanding of distributed systems concepts (e.g., MPP, Map Reduce) is a plus