Data platform engineer with cloud expertise

We are seeking a Data Platform Engineer to join our team based in Jersey City, NJ, for a long-term engagement lasting over 12 months. This role offers a competitive rate of $80/hour. Our company values innovative thinking and is committed to building a collaborative and inclusive environment. As a key player on our team, you will have the opportunity to work with cutting-edge technologies and contribute to our ongoing success.

- Over 3 years of experience in a Data Platform Engineering position
- Robust software engineering background with strong Python skills
- Significant expertise with SQL and database technologies such as MySQL, PostgreSQL, SQL Server, Snowflake, Redshift, Presto, etc.
- Proven experience constructing ETL and stream processing pipelines using tools like Kafka, Spark, Flink, Airflow/Prefect, etc.
- Familiarity with the data science toolkit, including Jupyter, Pandas, Scikit-learn, Dask, Pytorch, MLFlow, Kubeflow, etc.
- Extensive knowledge of AWS and Google Cloud Platform services (S3, EC2, IAM, etc.), Kubernetes, and Linux in a production environment
- Strong inclination towards automation and DevOps methodologies
- Experience handling increasing data volume, velocity, and diversity
- Agile mindset with a focus on delivering results
- Ability to manage uncertainty effectively
- Excellent communication skills
- Willingness to participate in on-call duties outside regular business hours
- Preferred Skills: Competency in developing with C++, JavaScript, Go, Rust
- Understanding of TCP/IP and distributed systems
- Experience in managing time series data
- Familiarity with site reliability engineering principles
- Background in Financial Services is a plus

- Build and maintain Tradewebs data platform leveraging technologies such as public cloud infrastructure (AWS and Google Cloud Platform), Kafka, databases, and containers
- Develop the data science platform for Tradeweb utilizing open-source software and cloud services
- Construct and manage ETL pipelines to integrate data into the platform, establish schemas, implement DAG processing pipelines, and monitor data quality
- Contribute to the development of frameworks and pipelines for machine learning
- Oversee and manage critical production services