Senior Data Platform Architect

Posted 2 days ago by Luxoft

Permanent
Full Time
Other
Not Specified, United Kingdom
Job Description
Overview

We are seeking an expert with deep proficiency as a Platform Engineer, possessing experience in data engineering. This individual should have a comprehensive understanding of both data platforms and software engineering, enabling them to integrate the platform effectively within an IT ecosystem.

Responsibilities
  • Manage and optimize data platforms (Databricks, Palantir).
  • Ensure high availability, security, and performance of data systems.
  • Provide valuable insights about data platform usage.
  • Optimize computing and storage for large-scale data processing.
  • Design and maintain system libraries (Python) used in ETL pipelines and platform governance.
  • Optimize ETL Processes - Enhance and tune existing ETL processes for better performance, scalability, and reliability.
Qualifications
  • Minimum 10 Years of experience in IT/Data.
  • Minimum 5 years of experience as a Data Platform Engineer/Data Engineer.
  • Bachelor's in IT or related field.
  • Infrastructure & Cloud: Azure, AWS (expertise in storage, networking, compute).
  • Data Platform Tools: Any of Palantir, Databricks, Snowflake.
  • Programming: Proficiency in PySpark for distributed computing and Python for ETL development.
  • SQL: Expertise in writing and optimizing SQL queries, preferably with experience in databases such as PostgreSQL, MySQL, Oracle, or Snowflake.
  • Data Warehousing: Experience working with data warehousing concepts and platforms, ideally Databricks.
  • ETL Tools: Familiarity with ETL tools & processes
  • Data Modelling: Experience with dimensional modelling, normalization/denormalization, and schema design.
  • Version Control: Proficiency with version control tools like Git to manage codebases and collaborate on development.
  • Data Pipeline Monitoring: Familiarity with monitoring tools (e.g., Prometheus, Grafana, or custom monitoring scripts) to track pipeline performance.
  • Data Quality Tools: Experience implementing data validation, cleaning, and quality frameworks, ideally Monte Carlo.
Nice to have
  • Containerization & Orchestration: Docker, Kubernetes.
  • Infrastructure as Code (IaC): Terraform.
  • Understanding of Investment Data domain (desired).