Our client is a private investment firm combining capital, strategic insight, and engineering capabilities to build and scale complex businesses. They manage diverse asset strategies and collaborate closely with portfolio companies on operational, simulation, and engineering infrastructure.
Responsibilities:
Lead the design, development, and maintenance of scalable data pipelines, ETL/ELT processes, and data models
Design and implement reusable data processing frameworks that support different data types and patterns
Develop and optimize data storage solutions including data warehousing, data lakes, and NoSQL databases on cloud platforms
Collaborate with data scientists, analysts, and business stakeholders to understand data needs and deliver solutions
Ensure data quality, governance, and security through the implementation of best practices and robust monitoring systems
Ensure efficient data ingestion, storage, and processing by using big data tools
Requirements:
Bachelor’s degree in computer science, engineering, or a related field
7-10 years of experience as a data engineer or in a similar role
Experience with data warehousing, ETL/ELT processes, and data modeling
Proficiency in Python programming
Hands-on experience with big data technologies such as Kafka, Spark, HDFS, Flink, Trino, Iceberg
Experience with AWS cloud platform
Strong SQL skills and experience working with both relational and NoSQL databases