Ghostless: tech jobs with the record job boards don't keep

Posting record

No flags

First seen today

How we know

It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.

Ghostless records the day it first sees a posting, so a repost can't reset its age.

Last checked Nov 4, 2026.

See how much of this posting your resume shows, and what it's missing.

5+ years of experience: “At least 5 years of hands-on experience in data engineering , particularly in designing, building, and maintaining production data pipelines and cloud-based data infrastructure.” (quoted from the posting)

Not stated: Pay, real vacancy, AI screening.

Why these three

Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.

Some employers add pay only on their own site. Check the original posting before you rule it out.

Own the reliability, scalability, and performance of the company's core data pipelines and infrastructure. Set technical priorities, identify risks, and drive improvements to ensure data remains accurate, accessible, and dependable as the business grows.
Lead the design and delivery of complex data engineering initiatives, from architecture and technical planning through implementation. Make sound technical decisions, balance competing priorities, and ensure solutions address immediate business needs while remaining scalable and maintainable.
Establish and reinforce engineering standards across Python, SQL, orchestration, testing, documentation, data governance, and monitoring. Leverage AI and automation to reduce repetitive tasks, such as test and documentation generation and early detection of data quality issues, allowing the team to focus on more complex engineering challenges.
Set the technical direction and quality standards for data engineering. Conduct design and code reviews, share technical knowledge, and support the onboarding, development, and guidance of engineers as the team grows.
Partner with Data Science, BI, and business teams to translate data requirements into practical technical solutions. Anticipate dependencies, resolve complex issues, and strengthen the data foundation supporting analytics, machine learning, and business decision-making.

At least 5 years of hands-on experience in data engineering , particularly in designing, building, and maintaining production data pipelines and cloud-based data infrastructure.
Strong hands-on experience with dbt (Data Build Tool) for data transformation, modelling, and management of analytics workflows.
Solid experience with Google Cloud Platform (GCP) , particularly BigQuery and other relevant data processing and storage services.
Proven experience using Apache Airflow for workflow orchestration, scheduling, monitoring, and managing data pipelines.
Strong proficiency in Python and SQL , with the ability to develop scalable data solutions and troubleshoot complex technical issues.
Good understanding of data architecture, data warehousing, ETL/ELT processes, performance optimisation, data quality, monitoring, and governance.
Experience designing technical solutions, leading complex data engineering projects, and taking ownership of delivery and ongoing system performance.
Ability to establish engineering standards, conduct code reviews, provide technical guidance, and share knowledge with other engineers.
Familiarity with AI-assisted development, automation, or modern engineering practices to improve development productivity, data quality, and operational efficiency.
Strong problem-solving and decision-making skills, with the ability to balance technical priorities and business requirements and communicate effectively with technical and nontechnical

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