AWS Data Engineer

Requirements:

• 2+ years of commercial experience with AWS (hands-on in data-related services: S3, RDS, Lambda, Glue, etc.)

• Solid experience with PostgreSQL (TimescaleDB a plus, not mandatory)

• Strong troubleshooting skills for cloud infrastructure and data systems

• Experience with ETL pipelines and scripting in Python (automation, data processing, monitoring)

• Ability to work independently and manage data workflows with minimal supervision

• Good English communication skills — direct collaboration with US-based team

Nice to Have:

• Familiarity with geospatial data

• Knowledge of AWS data ecosystem (Athena, Redshift, Kinesis, etc.)

• Experience with infrastructure-as-code tools (Terraform, CloudFormation)

• Exposure to monitoring solutions (CloudWatch, Grafana, or similar)

• Previous experience in a hybrid role (DevOps + Data Engineering)

Key Responsibilities:

• Design, operate, and optimize data pipelines in AWS

• Manage and improve data storage systems (PostgreSQL / TimescaleDB)

• Develop and maintain ETL workflows (Python scripts + AWS services)

• Build robust monitoring for data infrastructure (alerts, dashboards, stability tools)

• Troubleshoot data infrastructure issues and ensure uptime

• Suggest and implement improvements in AWS data architecture

• Collaborate with data analysts and project leads to ensure smooth delivery of data products

What You’ll Work With:

• Infrastructure: AWS (manual setup currently, automation planned)

• Database: PostgreSQL / TimescaleDB (TigerData)

• ETL: Python scripts (tool TBD, AWS-native tools possible)

• Monitoring: You choose & implement (full ownership)

• Orchestration: None yet (Docker/K8s not in use)

• CI/CD: Not currently used (no compiled codebase)

• Automation: Terraform/CloudFormation possible later

• Data: Geodata + time series streams (daily updates)

Why This Role?

• Direct ownership of a clean and buildable AWS data infrastructure

• Influence on tooling and architecture from day one

• Work on a focused data pipeline with flexibility in implementation