Employer description & source details
Build, expand, and optimize data infrastructure in order to create the most accurate dataset of identities and their relationships Develop and operate secure, scalable, and reliable data ingestion and ETL/ELT pipelines that meet product requirements Design and maintain a data observability framework to ensure our data meets strict quality and freshness standards Optimize data storage layer and build/maintain interfaces to enable scalable and fast data access to our data stores Collaborate with product teams (squads) supporting their data platform needs for smooth delivery of existing and new products Participate in call rotation for production issues, along with the rest of engineering Mentor junior engineers and contribute to our evolving engineering best practices Drive innovation by actively participating in our hackathons and doing proof of concepts Develop functional subject matter expertise within various areas of identity fraud domain 5+ years of experience in software engineering, data engineering or related field Proficient in in python or golang and related technologies and frameworks Expertise in building and maintaining ETL/ELT pipelines at scale leveraging distributed data processing technologies like spark, hadoop, kafka or similar technologies Hands-on experience with public cloud platforms such as AWS, Microsoft Azure or GCP Deep understanding of different database technologies including but not limited to RDBMS (e.g. postgres), NoSQL (OpenSearch, vector DB), Columnar data stores etc. and experience with writing efficient queries and optimization techniques Experience building enterprise grade, scalable, containerized data services and frameworks on Kubernetes or similar platforms Working knowledge of Infrastructure-as-Code and devops practices Excellent
Extracted skills: python, go, postgresql, aws, azure, gcp, docker, kubernetes, security