Senior Data Engineer

Euna Solutions•Oakville, ON

2d•CA$127,000 - CA$155,000•Hybrid

About The Position

Senior Data Engineers are the builders for the business. You are recruited early and help stand up the managed foundation alongside the architect, then build the data products that non-engineering teams need once the architecture is defined and the backlog is ranked. Product engineering teams build their own data products, and both build to the same architecture and patterns the architect defines. Your work begins on the foundation and moves into product delivery without a hard phase gate between the two. You build to the architect's patterns first. Most of the work is durable, governed analytical products; low-code activation is reserved for specific, safe use cases where it is genuinely the right call. You are trusted to know the difference and to build each one properly.

Requirements

Senior data engineering experience, typically 6+ years, building and operating production pipelines end to end, ideally on AWS.
Strong SQL and Python, and comfort with transformation tooling such as dbt.
Hands-on, production experience with managed AWS data services (S3, Glue, Athena, and related).
An instinct for governed, reproducible work: version control, lineage, testing, and documentation as defaults rather than afterthoughts.
The autonomy to take the defined architecture and roadmap and implement it end to end, without hand-holding.
The pragmatism to ship a low-code win fast when that is the right call, and the discipline to build the durable version when it is not.

Nice To Haves

Experience with low-code orchestration (n8n or similar) and connector-based integration.
Exposure to entity resolution, master data management, or building a customer master across multiple source systems.
Familiarity with data classification, PII handling, and residency controls in a regulated or public sector setting.
Redshift or comparable cloud data warehouse experience.

Responsibilities

Foundation build. Help the architect stand up the managed AWS foundation and the layered data zones, from raw through to the safe analytical layer.
Data product delivery. Build the top-ranked data products for non-engineering teams from the backlog, following the architect's patterns rather than inventing your own plumbing.
Low-code, for specific cases. Ship activation and light analytics through n8n, connectors, and AI, such as write-backs, alerts, and simple aggregations, where a fast, safe win is the right call.
Governed products, the default. Build the real analytical products on the managed foundation: reproducible, governed, and with full lineage.
Enabling product teams. Product engineering teams build their own customer-facing data products. Support them so they deploy on the same architecture and patterns the architect defines, through a governed contract, rather than a parallel approach.
Reliability discipline. Apply the guardrail honestly. If a workflow is high-criticality, high-volume, needs reproducibility or lineage, touches sensitive data, or lacks a clear owner, it belongs on the governed path, not a low-code shortcut.