Looking for
a Data Engineer to join our engineering will contribute directly to the design,
automation, and optimization of our data processes, primarily developing
solutions in Python within the AWS cloud ecosystem, including Lambda, Glue,
Redshift, and other key services.
The role
also involves working with infrastructure as code (Terraform), Git-based
version control, and designing scalable data architectures. The ideal candidate
has solid data engineering knowledge, a passion for automation, experience
working with large volumes of data, and the ability to proactively propose
technical and architectural improvements. Responsibilities:
Design,
develop, and maintain efficient ETL processes using Python, SQL, AWS Glue, and
Redshift.
Automate
data flows and integrations using AWS Lambda and other serverless services. Propose
improvements and optimizations to existing pipelines, prioritizing performance,
scalability, and maintainability.
Collaborate
on the design of scalable and resilient data architectures in AWS.
Develop and
manage infrastructure as code using Terraform.
Actively
participate in code reviews and collaborative version control workflows using
Git.
Document
technical solutions and promote best practices in data engineering.
Ensure that
data processing pipelines can handle large-scale datasets (Big Data). Who we’re
looking for: Technical Requirements:
Programming
Languages: o Python – 3+ years of experience (Intermediate to advanced level) o
SQL – 3+ years of experience, capable of working with complex data models
Large-scale data processing: o Experience with
PySpark or Big Data environments – 1–2+ years (preferred) o Familiarity with
distributed processing and performance optimization for high-volume data
pipelines
AWS
Technologies: o Lambda, Glue, Redshift, S3 – 2–3 years of hands-on experience o
Experience designing ETL workflows using native AWS services
Infrastructure
and DevOps: o Terraform – 1–2 years of experience (Intermediate level) o Git –
Daily use in collaborative development environments
Best
Practices: o Well-structured project organization, error handling, logging, and
automated testing within data pipelines Professional Profile:
Proactive
mindset with a drive to propose and implement improvements
Analytical
thinker with the ability to identify bottlenecks and performance issues Effective collaborator with both technical
teams and non-technical stakeholders
Strong
documentation and technical communication skills Nice to have (not mandatory):
Experience
with monitoring and observability tools for data pipelines (CloudWatch,
logging, alerting) Knowledge of event-driven architecture design
Familiarity
with orchestration tools like Apache
Experience
with automated testing for data pipelines
Background
in Fintech or experience handling financial datasets
AWS
certifications are a plus