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 innovation with maintainability and operational excellence.
Collaboration and Mentorship
- Work with engineers, data scientists, and product managers to translate business requirements into scalable, data-centric features.
- Mentor peers in software, data, and ML engineering best practices.
- Promote collaboration between feature and data teams.
About the Client
Our client is an Australian education technology company helping schools turn everyday learning into measurable progress. Its curriculum-aligned lessons, purposeful practice, and actionable insights support more than 1,000 schools, 17,000 teachers, and 200,000 students across Australia. Built on a decade of innovation and insights from over 1.7 billion data points, its platform helps educators identify learning needs and guide better decisions. Here, your engineering work contributes to a proven system that makes quality education more effective at scale each day.
Join the Top 1% Talent. A better career. A better life.
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