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Senior Data Platform Architect
Job summary
A leading technology consulting firm is seeking a seasoned Platform Engineer with a strong background in data engineering. You will manage and optimize data platforms like Databricks and Palantir, ensuring high availability and performance. The ideal candidate has at least 10 years of IT experience and expertise in cloud infrastructure. Strong proficiency in Python and SQL is essential, along with familiarity with data warehousing concepts and ETL processes.
Qualifications
- Minimum 10 years of experience in IT/Data.
- Minimum 5 years of experience as a Data Platform Engineer/Data Engineer.
- Expertise in Azure, AWS for cloud infrastructure.
Responsibilities
- Manage and optimize data platforms (Databricks, Palantir).
- Ensure high availability, security, and performance of data systems.
- Optimize ETL processes for better performance and reliability.
Skills
Education
Tools
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.
- 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.
- 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.
- Containerization & Orchestration: Docker, Kubernetes.
- Infrastructure as Code (IaC): Terraform.
- Understanding of Investment Data domain (desired).
* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.