Posting record
Open 17+ days
- ShyftLabs' site dates the posting
- Ghostless first sees this listing
- Last checked: still up
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. This one was already up when Ghostless started tracking ShyftLabs, so its true age is longer.
Last checked Nov 4, 2026.
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Upload your resumeWhat it asks of you
- 4+ years of experience
What the posting discloses
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.
Skills mentioned
About the role
Position Overview
We are looking for a Data Engineer with hands-on experience in building scalable data pipelines and data engineering solutions on the Databricks Lakehouse Platform. The ideal candidate should have strong expertise in Python, PySpark, SQL, Databricks, AWS, and REST API integrations for data ingestion, managing large volumes of data, and data export ShyftLabs is a growing data product company that was founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses in various industries, by focusing on creating value through innovation.
Job Responsibilities
Design, develop, and maintain scalable ETL/ELT pipelines using Databricks,
PySpark, and SQL.
● Integrate data from multiple sources, including databases, Amazon S3, files, and REST APIs.
● Build data pipelines with Databricks Unity Catalog.
● Implement business logic, data transformations, and dimensional data models.
● Create, schedule, monitor, and optimize Databricks Jobs and Workflows.
● Design and manage Delta Lake tables using Medallion Architecture (Bronze, Silver,Gold).
● Ensure data quality through validations, error handling, logging, and monitoring.
● Optimize Spark workloads for performance, scalability, and reliability.
● Collaborate with cross-functional teams to deliver production-ready data solutions.
Basic Qualification
Strong expertise in Python, PySpark, and Advanced SQL.
● Hands-on experience with the Databricks Lakehouse Platform.
● Good understanding of Unity Catalog, Delta Lake, Databricks Workflows/Jobs,
Clusters, Notebooks, Repos, and Medallion Architecture.
● Experience integrating with REST APIs for data ingestion and data export.
● Strong knowledge of ETL/ELT development, batch processing, incremental loading, and data transformation.
Data Governance experience would be an added advantage.
● Experience with data modeling (Star Schema, Snowflake Schema, Fact & Dimension tables, SCD concepts).
● Understanding of data warehousing concepts and best practices.
● Experience working with structured and semi-structured data (CSV, JSON, Parquet, Delta).
● Knowledge of partitioning, file optimization, Spark performance tuning, and query optimization.
● Experience with Git and CI/CD best practices
Preferred Qualifications
4+ years of experience in Data Engineering with 2+ years of hands-on Databricks
experience. Data Governance experience would be an added advantage.
● Experience with Auto Loader, Spark Declarative pipelines, Kafka, Airflow, or dbt is a plus.
● Databricks certification is an added advantage.
We are proud to offer a competitive salary alongside a strong insurance package. We pride ourselves on the growth of our employees, offering extensive learning and development resources.
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