Data Engineer

About Splitit:

Our mission is to help consumers responsibly enrich their lives by leveraging the credit they’ve already earned in a new and impactful way. Today consumers want control of how they pay for the things they want and need. Splitit is the only Installments-as-a-Service solution that enables consumers to use their existing credit to break payments into smaller, bite-sized pieces to pay over-time with no interest, applications or fees. That keeps money in the hands of shoppers to use as they want. By helping shoppers pay small we help businesses grow big. That’s a win-win. Splitit operates globally, with teams distributed around the world with offices in Atlanta (HQ) , Melbourne, and Tel Aviv.

Key Responsibilities:

ETL/ELT Design & Development:
Architect and implement scalable, end-to-end ETL/ELT pipelines.
Utilize Airflow to schedule, monitor, and manage complex workflows.
Integrate data sources using tools like Airbyte or Meltano.
Develop data transformation models using dbt, ensuring data quality and consistency.
Cloud Infrastructure Management:
Deploy, manage, and optimize data solutions on AWS, leveraging services such as S3 and Lambda.
Ensure secure, reliable, and cost-effective cloud operations.
Maintaining and developing data-related CI/CD workflows in Github Actions.
Containerization & Orchestration:
Build containerized applications using Docker.
deploying Docker applications to Kubernetes, ensuring high availability and performance, while collaborating with DevOps to resolve issues
Performance & Monitoring:
Continuously optimize data processing systems for performance, scalability, and reliability.
Implement monitoring solutions and proactively troubleshoot issues in production.
Collaboration & Documentation:
Work closely with data analysts, product team, developers and other stakeholders to align data strategies with business objectives.
Maintain clear documentation of data architectures, pipelines, and processes.

Required Qualifications:

Experience:
4+ years of hands-on experience in data engineering with a focus on designing and maintaining data infrastructure.
Technical Proficiency:
Strong programming skills in Python.
Proven expertise in workflow orchestration using Airflow.
Experience with data integration tools (e.g. Airbyte, Meltano, dlt) and transformation frameworks (e.g. dbt).
Solid background in AWS cloud services.
Write and optimize advanced SQL queries to support analytics.
Hands-on experience with containerization (Docker) and orchestration (Kubernetes).
Experience working with relational databases (e.g. PostgreSQL, MSSQL), analytical databases (e.g. Snowflake), and NoSQL databases (e.g. MongoDB), including performance & cost optimization, and query tuning.
Foundational Knowledge:
Deep understanding of data modeling, ETL/ELT processes, data warehousing concepts, and the consequence of decisions made here on the wider business.
Additional Skills:
Excellent problem-solving, analytical, and communication skills.
Comfortable prioritising tasks based on business context.
Ability to work collaboratively in agile, cross-functional teams.