Requirements

Expected technologies

Python

AWS

SQL

DWH

Data modeling

DBT

Optional technologies

DBT

Java

Our requirements

• 5+ years of professional experience as a Data or Python Engineer
• Experienced in leading design reviews, engaging in pair programming, and documenting key decisions for long-term clarity.
• Experience in Data Engineering – including schema design, query optimization, and managing 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.
• You’re familiar with dbt or eager to work with it
• Strong background in building and maintaining RESTful APIs, microservices, and backend systems.
• Have acted as a technical/feature lead on multiple projects, owning solutions from design through production operation.
• Comfortable working closely with the Product team, aligning goals, making independent technical decisions, and challenging assumptions when needed.

Optional

• Experience in Java

Your responsibilities

Leadership, Innovation & Excellence

Drive technical design discussions, lead critical implementations, and support decision-making across the team Mentor other engineers and raise the bar for code quality and system thinking Stay current with evolving technologies and proactively introduce improved tools, patterns, or approaches when beneficial. Champion engineering excellence by questioning the status quo and influencing product/technical direction

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

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

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

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

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