Ccgmag
About the Role
You'll design and develop modern data infrastructure and applications, enabling portfolio managers and researchers to access high-quality, scalable data solutions. The role involves working across ingestion, ETL, data quality, storage, and APIs, while collaborating closely with quantitative engineers, researchers, and technical teams in a global environment.
Key Responsibilities
Build and optimize data pipelines and platforms for large-scale, complex datasets.
Design and implement robust ETL processes and data models for batch and streaming workflows.
Collaborate with quants and engineers to deliver scalable solutions aligned with business needs.
Develop APIs and tools to enable efficient data consumption and integration.
Ensure performance, reliability, and scalability across distributed systems.
Ideal Candidate Profile
5+ years of experience in data engineering or similar data-intensive roles.
Strong Python skills and experience with data libraries (e.g., Pandas, Polars, Dask, PySpark).
Expertise in SQL and relational databases; familiarity with columnar formats (Parquet/Arrow).
Experience with distributed systems and messaging tools (e.g., Kafka, Redis).
Knowledge of data lake/warehouse architectures and performance tuning.
Excellent problem-solving and communication skills.
Bonus: Experience with REST APIs, monitoring tools (Prometheus, Grafana), and financial datasets.
If you feel the role is a good fit - apply today!
#J-18808-Ljbffr
Key Responsibilities
Build and optimize data pipelines and platforms for large-scale, complex datasets.
Design and implement robust ETL processes and data models for batch and streaming workflows.
Collaborate with quants and engineers to deliver scalable solutions aligned with business needs.
Develop APIs and tools to enable efficient data consumption and integration.
Ensure performance, reliability, and scalability across distributed systems.
Ideal Candidate Profile
5+ years of experience in data engineering or similar data-intensive roles.
Strong Python skills and experience with data libraries (e.g., Pandas, Polars, Dask, PySpark).
Expertise in SQL and relational databases; familiarity with columnar formats (Parquet/Arrow).
Experience with distributed systems and messaging tools (e.g., Kafka, Redis).
Knowledge of data lake/warehouse architectures and performance tuning.
Excellent problem-solving and communication skills.
Bonus: Experience with REST APIs, monitoring tools (Prometheus, Grafana), and financial datasets.
If you feel the role is a good fit - apply today!
#J-18808-Ljbffr