Lead Data Platform Engineer/Architect

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).