Senior Software Engineer - Data Platform & AI Automation

Diligent Corporation•Vancouver, BC

1d•CA$114,240 - CA$140,000•Hybrid

About The Position

We're hiring a dedicated data engineer to own the production data platform that our delivery, product, and engineering teams run on; designing integrated, governed data pipelines and delivering automated reporting, AI-assisted workflows, and predictive signals on top of them. You'll write production code, design systems, own CI/CD, and be accountable for the correctness of data that leaders make decisions on.

Requirements

Strong software engineering fundamentals: production-quality code, API and interface design, testing discipline, systems design.
Real experience building and operating production data platforms on a cloud warehouse or lakehouse (Snowflake and AWS preferred) with dbt and a modern orchestrator.
Practical AI tooling experience: something shipped, not prototyped. LLM-backed classification, extraction or structured-output pipelines; agent and tool-calling workflows; retrieval; evals.
You can reason about cost, latency, failure modes and when not to use a model.
Sound governance judgement: data quality, observability, lineage, versioned contracts, auditability, PII handling.
CI/CD and DataOps: automated testing, release gates, infrastructure automation, rollback.
Comfort working directly with engineering, product, finance and security stakeholders.

Nice To Haves

Feature stores and MLOps tooling: serving and monitoring models in production
Experience working with and designing MCP Servers
Experience with BI platforms and semantic layers
You already use AI coding tools as part of how you work

Responsibilities

Design and operate our cloud data platform: ingestion, transformation, orchestration and serving.
Integrate data from across the business (delivery tooling, CRM, product telemetry, finance, support and customer feedback systems) with shared identifiers, data contracts and lineage.
Build automated and continuously refreshed reporting so teams manage by exception rather than chasing status.
Connect approved AI agents to governed data with structured outputs, provenance, guardrails and human approval in the loop.
Build feature pipelines and the MLOps controls behind predictive use cases: tests, versioning, promotion gates and drift monitoring.
Own the engineering standards for data: testing, observability, environment promotion, PII classification and access control.