Requirements

Expected technologies

Python

AWS

SQL

Docker

DWH

DBT

Data Modeling

Our requirements

• 3+ years of experience as a Data Engineer.
• Hands-on experience with schema design, complex SQL/query optimization, and running data pipelines in production.
• Experienced with AWS services (Redshift, Aurora, DynamoDB, S3, Glue, Lambda, Step Functions, etc.) to build data pipelines and scalable cloud-native applications.
• dbt experience (or strong SQL/ELT background and eagerness to learn dbt quickly).
• Familiarity with data orchestration tools (e.g., Airflow, Step Functions) — scheduling, monitoring, and troubleshooting data pipelines.
• Ability to build and maintain RESTful APIs/microservices in Python (e.g., FastAPI/Flask) and understand basic backend architecture.

Optional

• Experience in Java is a plus.

Your responsibilities

1. Backend System Development

- Design, build, and maintain robust Python-based services and microservices

- Develop and optimize RESTful APIs and background services supporting core business logic and integrations

- Ensure code quality, reusability, and scalability through modular design and adherence to best practices

2. Cloud-Native Application Engineering

- Develop serverless and containerized applications using AWS Lambda, ECS, and other cloud-native tools

- Leverage AWS services (S3, RDS, DynamoDB, Step Functions, etc.) to support backend operations and workflows

- Collaborate with DevOps to provision, deploy, and monitor cloud infrastructure

3. Automation and Task Orchestration

- Automate recurring tasks, background jobs, and workflows using Python scripts and AWS orchestration tools

- Build and maintain task schedulers and asynchronous workers for time-sensitive operations

- Implement monitoring, logging, and alerting systems for observability and proactive issue resolution

4. Data Access and Integration

- Build data access layers and connectors for interfacing with relational and NoSQL databases

- Develop data integration scripts or services to move and sync data between systems when needed

- Write efficient, production-grade SQL and Python code to support internal tools and services

5. Performance and Reliability

- Optimize application performance, API response times, and system throughput

- Implement retries, fallbacks, and circuit breakers to increase fault tolerance

- Continuously assess and improve system design for scalability and maintainability

6. Contribute to Innovation and Excellence

- Stay informed on modern Python practices, libraries, and AWS developments

- Take initiative in proposing improvements and new ideas to enhance our platform