Senior Software Developer, Backend

Autodesk•Toronto, ON

2d•CA$107,000 - CA$157,300•Onsite

About The Position

The Autodesk Info360 Operational Analytics team is building the data infrastructure that powers modern water management, helping utilities monitor, predict, and respond to issues across thousands of sensors in near real-time. We're looking for a Senior Backend / Data Developer who thrives on high-frequency, large-scale data problems. You'll design and ship the systems behind time-series analytics, smart alerting, and anomaly detection features that water operators depend on every day. This is a backend-heavy role with strong data engineering depth. You'll own pipelines, APIs, and the reliability of the data that drives critical decisions.

Requirements

5+ years of experience building and shipping large-scale backend systems in production
Strong experience with data pipelines, streaming architectures, or large-dataset processing (Kafka, Kinesis, Snowflake, Flink or similar)
Hands-on experience with time-series data (storage, querying, or analytics at scale)
Solid systems design skills: distributed systems, event-driven architectures, service-oriented backends
Experience with cloud infrastructure, ideally AWS (Lambda, RDS/Aurora, DynamoDB, S3, or similar)
Familiarity with ML infrastructure concepts: building systems that support model integration
Strong observability and debugging instincts. You build with monitoring in mind from day one
Ability to drive technical alignment across teams and communicate clearly about tradeoffs
Degree in Computer Science or related field, or equivalent practical experience

Responsibilities

Design, build, and scale cloud-native backend services and APIs on AWS that handle high-frequency sensor and time-series data
Build and optimize data pipelines and ETL/ELT processes for large-scale, near real-time ingestion and processing
Play a key role in delivering AI/ML-powered features by building the backend systems, data pipelines, and APIs that bring intelligent capabilities to production
Own features end-to-end from design through production including monitoring, reliability, and iteration
Identify and resolve bottlenecks in data flow, query performance and service interactions
Build backend infrastructure that supports ML model serving, feature pipelines, and feedback loops for predictive alerting
Leverage AI-assisted development practices to accelerate design, implementation, and code quality across backend services and pipelines
Collaborate across engineering, product, and AI/ML teams to enable seamless integration across services and platforms
Raise the technical bar through design reviews, code reviews, and mentoring peers