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Data Engineer

Citylitics
Toronto, CAN
Full-time
Onsite
Discovered 3 weeks ago
PythonSQLData engineeringData pipeline orchestrationApache AirflowCloud Composer

Full Job Posting

About Citylitics

Citylitics turns documents from cities, utilities, and public agencies into infrastructure investment signals and sales intelligence.

Its platform uses AI agents, fine-tuned models, a proprietary infrastructure signals graph, and human quality assurance; it integrates with CRMs, ERPs, and data lakes.

Role overview

The Data Engineer will build and maintain mission-critical data products and production pipelines using AI for customer-facing solutions.

The role requires hands-on experience putting LLM and agentic workflows into production.

This is a hybrid role based out of the Toronto office, requiring at least 3 days in-office per week.

Responsibilities

  • Develop and own Airflow and BigQuery pipelines through design, testing, deployment, monitoring, and maintenance.
  • Design scalable data products with analysts and stakeholders, spanning data models, APIs, dashboards, and applications.
  • Build document-processing workflows using LLMs and agents, including prompts, retrieval, structured outputs, and production safeguards.
  • Maintain evaluation datasets, automated quality checks, and benchmarks.
  • Continuously improve data infrastructure, processes, and use of coding agents such as Claude Code.

Requirements

  • Proficiency in Python and SQL.
  • At least 3 years working as a Data Engineer or Software Engineer in a cloud environment.
  • Experience with production pipelines, an orchestrator such as Airflow or Cloud Composer, and a data warehouse such as BigQuery.
  • Strong data engineering fundamentals, including data modeling, ETL/ELT, and reliable idempotent pipelines.
  • The provided description cuts off during the requirements, so further qualifications may be missing.

Technologies and methods

  • The description names GCP services including Cloud Composer, Cloud SQL, BigQuery, and Vertex AI.
  • Workflow topics include tool and function calling, embeddings and retrieval, structured outputs, retries, idempotency, cost, and latency.
  • The team uses Claude Code for development, debugging, review, and documentation.

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