Salary
💰 $199,000 - $262,000 per year
Tech Stack
AirflowAWSCassandraETLHadoopJavaKafkaNoSQLPythonSparkSQLTerraform
About the role
- Design, implement, and maintain high-quality data infrastructure services, including Data Lake, Kafka, Amazon Kinesis, and data access layers
- Develop robust and efficient DBT models and jobs to support analytics reporting and machine learning modeling
- Closely collaborate with the Analytics team for data modeling, reporting, and data ingestion
- Create scalable real-time streaming pipelines and offline ETL pipelines
- Design, implement, and manage a data warehouse that provides secure access to large datasets
- Continuously improve data operations by automating manual processes, optimizing data delivery, and redesigning infrastructure for greater scalability
- Create engineering documentation for design, runbooks, and best practices
- Shape the future of finance data platforms to enable Machine Learning and Business Intelligence across the company
Requirements
- A minimum of 8 years of industry experience in the data infrastructure/data engineering domain
- A minimum of 8 years of experience with Python and SQL
- Java experience is a plus
- A minimum of 4 years of industry experience using DBT
- A minimum of 4 years of industry experience using Snowflake and its basic features
- A minimum of 4 years of industry experience using Infrastructure as Code tools, specifically CDK and Terraform
- Strong written and verbal communication skills for key collaboration
- Familiarity with AWS services (Lambda, Step Functions, Glue, RDS, EKS, DMS, EMR)
- Industry experience with big data platforms and tools such as Kafka, Hadoop, Hive, Spark, Cassandra, Airflow
- Industry experience working with relational and NoSQL databases in a production environment
- Strong fundamentals in data structures, algorithms, and design patterns
- Prior experience working on cross-functional teams
- Experience with CI/CD to improve code stability and code quality
- Motivated to help other engineers succeed and be effective
- Comfortable working in an ambiguous, fast-paced, high-growth environment