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What the market looks like

We've tracked 1,300+ mid-level data engineering postings across Canada in the last six months, with strongest hiring concentrated in Toronto, Vancouver, and Montreal across IT Services, Technology, Financial Services, and Professional Services sectors. Compensation for mid-level specialists typically lands between $120,000 and $230,000, with a median of around $130,000. The strongest candidates bring hands-on experience building and maintaining data pipelines at scale, deep fluency in cloud platforms (AWS, Azure, or GCP), and a track record of collaborating with analytics and ML teams to unblock downstream work — they ship data infrastructure that other teams depend on.

Job responsibilities

Design, build, and maintain scalable data pipelines and ETL processes that move data across multiple sources and systems
Own data quality, governance, and documentation standards to ensure downstream teams (analytics, ML, business intelligence) can trust and use the data
Optimize database performance and data warehouse architecture to support growing query volume and analytical workloads
Partner with data analysts, data scientists, and business stakeholders to understand requirements and translate them into robust technical solutions
Troubleshoot production data issues, implement monitoring and alerting, and lead incident response when pipelines fail
Lead code reviews and mentor junior engineers on data engineering best practices and tooling
Evaluate and integrate new data tools and technologies to improve engineering velocity and system reliability

Candidate requirements

5+ years of professional data engineering experience, including hands-on work building and operating production data pipelines
Strong SQL skills and deep experience with at least one major cloud data warehouse (Snowflake, BigQuery, Redshift, or equivalent)
Proficiency in at least one modern programming language (Python, Scala, Java, or Go) used in data engineering contexts
Demonstrated experience designing and implementing data models, managing schemas, and ensuring data consistency across systems
Ability to communicate technical trade-offs and data architecture decisions clearly to both technical and non-technical stakeholders
Track record of shipping infrastructure improvements that directly reduced operational toil or improved data accessibility for downstream teams

Senior Data Engineer