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- Employment type
- Full-time
- Experience level
- Mid-level · 2+ years
- Apply by
- Apr 14, 2027
- Posting language
- English
- Working hours
- 40 hours per week
- Seniority
- Entry level
Job summary
Design, build, and operate lakehouse data pipelines and curated data models for analytics and AI, including batch and streaming ingestion, production-grade transformations, and performance and cost optimization. Implement data quality, governance, security, observability, and CI/CD practices; migrate legacy warehouse workloads and collaborate with client teams.
Job details
About the role Augmented is hiring a Data Engineer to build and run a modern lakehouse platform for one of our large enterprise clients in a regulated industry. You will design reliable batch and streaming pipelines, model data for analytics and AI use cases, and help retire legacy warehouse workloads. You will join a small, senior delivery team that works directly with client stakeholders. The work spans greenfield builds and migrations, with a strong focus on data quality, governance and cost. Key responsibilities Design and build lakehouse pipelines using a medallion (bronze/silver/gold) architecture on Delta Lake or Apache Iceberg Develop batch and streaming ingestion from databases, APIs, files and event streams (CDC, Kafka) Write production-grade PySpark and SQL transformations; tune Spark jobs for performance and cost Model curated data sets (dimensional, Data Vault or wide tables) for BI, analytics and ML teams Implement data quality checks, lineage and observability; own pipeline SLAs and incident response Apply governance and security controls: Unity Catalog or equivalent, row/column-level access, PII handling Migrate legacy warehouse and ETL workloads (e.g. Teradata, Oracle, SSIS, Informatica) to the lakehouse Build CI/CD for data code and infrastructure-as-code for platform resources Partner with client architects, analysts and data scientists; document designs and decisions Required qualifications 4+ years in data engineering, with 2+ years on a lakehouse platform (Databricks, or Spark with Delta Lake/Iceberg) Strong Python (PySpark) and advanced SQL Hands-on with at least one major cloud: Azure, AWS or GCP (storage, compute, IAM, networking basics) Experience with orchestration such as Databricks Workflows, Airflow or Azure Data Factory Solid grasp of data modelling, partitioning, file formats (Parquet) and Spark performance tuning Git-based workflows and CI/CD (GitHub Actions, Azure DevOps or GitLab) Clear communicator, comfortable working directly with enterprise client teams Legally entitled to work in Canada Nice to have Databricks Certified Data Engineer (Associate or Professional), or equivalent cloud data certification dbt, Delta Live Tables / Lakeflow, or Spark Structured Streaming in production Terraform or Bicep for platform provisioning Experience in Life Sciences (GxP, clinical or commercial data) or Financial Services (risk, payments, regulatory reporting) Exposure to feature stores, vector search or data products for AI/ML Consulting or client-facing delivery background Tech stack • Platform: Databricks (Unity Catalog), Delta Lake / Apache Iceberg • Languages: Python (PySpark), SQL • Cloud: Azure (ADLS Gen2) or AWS (S3) • Ingestion & streaming: Kafka / Event Hubs, CDC tools, Auto Loader • Orchestration: Databricks Workflows, Airflow, Azure Data Factory • Transformation: Spark, dbt, Delta Live Tables • DevOps: Git, CI/CD pipelines, Terraform • Consumption: Power BI, Tableau, Databricks SQL Why Augmented Augmented is an AI-native engineering delivery hub serving large enterprises in Life Sciences and Fintech, with teams in Toronto and the US. Work on high-impact data platforms for enterprise clients Small, senior teams with direct access to client decision-makers Exposure to AI-native delivery practices and modern tooling Compensation: [salary range to confirm] plus benefits
What you’ll do
Design, build, and operate lakehouse data pipelines and curated data models for analytics and AI, including batch and streaming ingestion, production-grade transformations, and performance and cost optimization. Implement data quality, governance, security, observability, and CI/CD practices; migrate legacy warehouse workloads and collaborate with client teams.
Requirements
Requires 4+ years of data engineering experience, including 2+ years with a lakehouse platform, strong Python/PySpark and advanced SQL, and hands-on experience with a major cloud provider. Candidates should also have experience with orchestration, data modeling, Spark tuning, Git-based CI/CD, and direct communication with enterprise clients, and must be legally entitled to work in Canada.
Listed skills
- SQL · Preferred
- CI/CD · Preferred
- Terraform · Preferred
- Python · Preferred
Other relevant skills
Identified from the job description. Confirm important requirements above.
- Data Engineering
- Databricks
- Delta Lake
- Apache Iceberg
- PySpark
- Python
- SQL
- Batch and Streaming Pipelines
- Data Modeling
- Spark Performance Tuning
- Data Quality and Governance
- Cloud Platforms
- Data Orchestration
- CI/CD
- Terraform
- Data Migration
Job areas
- Data & Analytics
- Technology
- Software
- Consulting