Engineering Manager, Data
AgencyAnalytics
- Department
- Engineering
- Employment Type
- Permanent
- Location
- Remote Canada
- Workplace type
- Fully remote
- Compensation
- $120,000 - $180,000 / year
- Reporting To
- Criana Parsaud
Key Responsibilities
- Lead, coach and grow a team responsible for data ingestion, transformations, integration connectors, and data warehouse.
- Be a hands-on manager: split time between people management and technical work (architecture, code reviews, system design, and occasionally shipping code yourself).
- Own delivery and outcomes for pipeline/integration and data warehouse projects. Set priorities with Product, remove blockers, and protect engineering focus.
- Design and evolve reliable, observable, and secure data pipelines, data schemas, and integrations.
- Drive best practices for data quality, schema evolution, idempotency, retries, backfills, and monitoring/alerting.
- Collaborate with AI focused teams and Platform teams to design data contracts and interfaces that enable analytics and AI experiences (feature stores, embeddings, semantic indices).
- Make architectural decisions with an eye toward scale, operability, cost, and maintainability; partner closely with Staff engineers on long-term architectural work.
- Recruit, mentor, and develop engineers, helping them grow technically and in their careers
- Establish SLOs/SLIs for pipeline reliability, latency, and throughput; lead team incident response measures and postmortems.
- PHP 8.x with Laravel
- Typescript, React, and Redux
- GCP Cloud SQL (MySQL), BigQuery, and Redis
- GCP Cloud Tasks and PubSub
Skills, Knowledge & Expertise
- Experience running a data pipelines / integrations team (ownership of ingestion, ETL/ELT, or connector platforms).
- At least 1 year in an Engineering Manager role. Prior technical leadership (Tech Lead, Staff Engineer) is strongly preferred.
- Familiarity with cloud data warehousing and analytics platforms (BigQuery), strong operational experience with relational databases (MySQL, Postgres), and caching systems (Redis).
- Experience with API-first connector platforms and SaaS integrations.
- Recent experience building data context for AI apps: feature stores, embeddings, vector DBs, semantic layers, or tooling that prepares data for LLMs and retrieval-augmented generation is a big plus.
- Strong track record of delivering data systems that are reliable, observable, and maintainable.
- Proven ability to manage tradeoffs between speed, correctness, and cost at scale.
- Comfortable writing and reviewing production SQL, performing database tuning, migrations, and schema design. You want to stay technical.
About AgencyAnalytics
We're a founder-led Canadian success story that started in 2010, and we're over 140 strong today.
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Our Hiring Process
Applied
Talent Interview
Hiring Manager Interview
Final Interview
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