Toronto, Ontario, Canada
Open 6 months, first seen by us 185 days agoWork setup in the employer’s posting: RemoteFrom the employer’s posting
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Build cloud ETL/ELT processes across BigQuery, Cloud Storage, and Dataflow
Integrate OCR, NLP entity and Document AI tools to turn unstructured files into structured data
Tune complex SQL and Python jobs to run efficiently on petabyte-scale data
Work with data scientists and analysts to keep data flowing for decisions
Build and maintain cloud-native ETL/ELT processes using BigQuery, Cloud Storage, and Dataflow
Instead of just using LLMs, you will integrate AI tools (OCR, NLP entities, Document AI) into the engineering flow to transform unstructured blobs into structured insights.
Tune complex SQL queries and Python-based processing jobs to handle petabyte-scale environments efficiently.
You will collaborate closely with data scientists, analysts, and other stakeholders to ensure efficient data flow and support data-driven decision making across the organization.
From the employer’s posting.
Open 6 monthsWe first saw this posting 185 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 185
Up longer than most. Similar jobs usually come down between day 6 and day 56.
Well past Tiger Analytics’s usual: its jobs come down by about day 68.
From 13,355 similar postings that came down, and 27 of Tiger Analytics’s. Coming down is not always a hire.
74 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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- Instead of just using LLMs, you will integrate AI tools (OCR, NLP entities, Document AI) into the engineering flow to transform unstructured blobs into structured insights. - Tune complex SQL queries and Python-based processing jobs to handle petabyte-scale environments efficiently. Benefits
- Leverage Snowflake’s unstructured data capabilities (Directory Tables, Scoped URLs, Snowpark) to make "dark data" queryable and actionable. - Build and maintain cloud-native ETL/ELT processes using BigQuery, Cloud Storage, and Dataflow, ensuring seamless integration between GCP and Snowflake. - Instead of just using LLMs, you will integrate AI tools (OCR, NLP entities, Document AI) into the engineering flow to transform unstructured blobs into structured insights.
Requirements - Design and implement robust data pipelines that ingest, process, and store unstructured data formats at scale within Snowflake and GCP. - Leverage Snowflake’s unstructured data capabilities (Directory Tables, Scoped URLs, Snowpark) to make "dark data" queryable and actionable.
- Design and implement robust data pipelines that ingest, process, and store unstructured data formats at scale within Snowflake and GCP. - Leverage Snowflake’s unstructured data capabilities (Directory Tables, Scoped URLs, Snowpark) to make "dark data" queryable and actionable. - Build and maintain cloud-native ETL/ELT processes using BigQuery, Cloud Storage, and Dataflow, ensuring seamless integration between GCP and Snowflake.
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New Data Engineer postings at the employers we follow are up 7% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best analytics global consulting team in the world.
We are seeking an experienced Data Engineer with expertise in Dataiku to join our data team. As a Data Engineer, you will be responsible for designing, building, and maintaining data pipelines, data integration processes, and data infrastructure. You will collaborate closely with data scientists, analysts, and other stakeholders to ensure efficient data flow and support data-driven decision making across the organization.
Requirements
Benefits
Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, challenging, and entrepreneurial environment, with a high degree of individual responsibility.
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Toronto, Ontario, Canada
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