Engineering and Quality - Data Engineer
Tasks
- Adopt AI agent and generative AI capabilities in data workflows
- Apply security controls for data access and observability
- Build AWS based enterprise data platform
- Build ETL ELT pipelines for batch and streaming
- Design REST and GraphQL APIs for low latency reads
- Develop data ingestion transformation and distribution frameworks
- Implement data quality controls and reliability monitoring
- Implement scalable data solutions
- Maintain CI CD and infrastructure as code deployments
- Optimize object and relational datastore performance
Perks/Benefits
- Employee resource groups
- Flexible-hybrid work
- Global parental leave
- Health insurance
- Paid training and mentorship programs
- Paid volunteer days
- Retirement planning
- Tuition reimbursement
- Work-life balance
Skills/Tech-stack
AWS | AWS Glue | AWS KMS | AWS Lambda | Alation | Amazon Aurora | Amazon EKS | Amazon EMR | Amazon EventBridge | Amazon MSK | Amazon RDS | Amazon Redshift | Amazon S3 | Apache Kafka | ArgoCD | Avro | CI/CD | Collaboration | Collibra | Containers | Cost governance | Data Catalogs | Data Governance | Data Management | Data Quality | Docker | ELT | ETL | FastAPI | Generative AI | GitHub Actions | GraphQL | IAM | Informatica | Infrastructure as Code | JSON | Kafka Schemas | Langchain | Llamaindex | Master Data | Master Data Management | Microservices | OpenTelemetry | Pandas | PySpark | Python | RAG | REST APIs | Redis | SLOs | Secrets management | Step Functions | VPC | Valkey | “as-code”
Education
N/A
Roles
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