Senior Software Engineer

Engineer Smarter Learning for Australia's Education Technology Sector

Education technology matters most when it helps teachers act with confidence. In the Senior Software Engineer - Data & ML role, you'll develop scalable services and ML infrastructure that translate learning data into practical support. Your work will reach schools across Australia, with strong technical collaboration, meaningful ownership, and a rewarding long-term global career at Emapta for top 1% talent.

Snapshot

• Employment Type: Full-time
• Work Setup: Permanent Work From Home
• Shift: Day Shift, 10:00 AM-7:00 PM Melbourne Time, Weekends Off

Benefits

• Day 1 HMO coverage with free dependent
• Competitive Salary Package
• Day Shift schedule
• Fixed weekends off
• Permanent WFH arrangement
• Salary Advance Program through our banking partner (Eligibility and approval subject to bank assessment. Available to account holders with minimum of 6 months company tenure.)
• Unlimited upskilling through Emapta Academy courses (Want to know more? Visit: https://emapta.com/training-calendar/)
• Free 24/7 access to our office gyms (Ortigas and Makati) with a free physical fitness trainer
• Exclusive Emapta Lifestyle perks (hotel and restaurant discounts, and more)
• Unlimited opportunities for employee referral incentives across the organization
• Standard government and Emapta benefits
• Total of 20 annual leaves to be used on your own discretion (including 5 credits convertible to cash)
• Fun engagement activities for employees
• Mentorship and exposure to global leaders and teams
• Career growth opportunities
• Diverse and supportive work environment

Qualifications

• 3-5 years of relevant software engineering experience
• Strong Python engineering skills and experience with data and ML libraries such as Pandas, PySpark, Scikit-learn, and MLflow
• Backend development expertise with FastAPI, Django, Flask, or an equivalent framework, including API design and data system integration
• Experience developing ETL workflows and data pipelines with tools such as Airflow, Dagster, dbt, and n8n
• Strong understanding of data modeling and warehousing using SQL, DuckDB, and Parquet
• Experience deploying and scaling batch and real-time ML models in production
• Familiarity with AWS data and compute services, including S3, Glue, Lambda, Step Functions, ECS/Fargate, and RDS or Redshift
• Experience with CI/CD, Docker, and Infrastructure as Code using Terraform or CloudFormation
• Strong knowledge of data privacy, security, and compliance practices
• Experience implementing observability for backend and ML systems, including monitoring dashboards and logging
• Excellent communication, problem-solving, and cross-functional collaboration skills
• Experience improving engineering and data practices and mentoring peers
• Curiosity about emerging data and ML technologies and their applications in education

Responsibilities

Feature and Platform Development

• Build backend features and services that use data and ML insights to improve learning and assessment experiences.
• Collaborate with product and design teams to develop data-driven platform features.
• Contribute to shared libraries, APIs, and infrastructure that support rapid, reliable feature delivery.

Data Platform and ML Infrastructure

• Design, build, and maintain data pipelines and services for analytics, reporting, and ML use cases.
• Collaborate with data scientists on feature engineering, model training pipelines, deployment, monitoring, and retraining.
• Establish best practices for model lifecycle management, reproducibility, and production performance.

Backend Engineering for Data Systems

• Develop APIs and batch and streaming systems that process large volumes of educational and behavioral data.
• Optimize transactional and analytical workloads for performance, reliability, and scalability.
• Integrate data and ML outputs into user-facing systems through well-structured backend interfaces.

Data Governance, Quality, and Security

• Implement data validation, quality assurance, and lineage tracking to maintain trust in key data assets.

Technical Architecture and Strategy

• Help define the long-term architecture for data-driven and ML-enabled systems aligned with the platform's learning outcomes vision.
• Evaluate and integrate emerging data and ML technologies to improve scalability, observability, and developer productivity.
• Balance innovatio