Senior Data Engineer (AWS & Python)

On behalf of our oil & gas client, Vaquarii is seeking a Senior Data Engineer (AWS & Python) for a 12-Month Contract with a high possibility of extension or conversion to full-time employment The perfect candidate will build and maintain production-grade ETL/ELT pipelines on AWS that ingest data from the client’s disparate industrial source systems, consolidate it into a unified environment, and transform it into clean, accessible assets.

Services would be carried out though a Hybrid Work Schedule from their downtown Calgary headquarters.

Responsibilities:

• AWS is the primary cloud platform.
• Core services in active scope: S3, Glue jobs, DynamoDB, batch processing infrastructure. Broader AWS data service knowledge is a material advantage. Knowledge of AWS deployment automation, Lambda, and Batch are relevant to the role.
• Python is the primary language at an expert level.
• SQL is required at a performance tuning level — query execution plan analysis, optimization techniques, and complex manipulation.
• Writing queries that work is not sufficient; writing queries that perform at scale is the standard.
• Active database technologies in scope: relational databases (strong requirement), NoSQL (DynamoDB, MongoDB in use), Graph DBs (a plus, not a hard requirement).
• Spark knowledge is also a plus.
• Databricks is under active evaluation as a rapid prototyping environment.
• Candidates with Databricks production experience have a measurable advantage in the near term.
• MS Fabric is explicitly listed in the posting as a preferred qualification.
• CI/CD pipelines and automated testing are required practices.
• Candidates who write working scripts without testing frameworks, version control discipline, or modular architecture are not production-grade engineers for this environment.

Must Have Skills:

1. 3+ years in a Data Engineering role or Software Developer role with strong focus on data backend development and transformation.
2. Production pipeline delivery — scripting, analytics, or BI work does not qualify.
3. Python at an expert level.
4. SQL with demonstrated performance tuning capability — execution plan analysis and query optimization, not just query writing.
5. AWS hands-on experience: S3, Glue, and deployment automation at minimum. Candidate must be able to describe production implementations, not theoretical familiarity.
6. Software engineering discipline: CI/CD pipelines, automated testing, version control, modular code design. Working scripts that cannot be tested, maintained, or handed to another engineer are a disqualifier.
7. Production ETL/ELT pipeline construction — automated, end-to-end, with demonstrated delivery evidence.

Nice to Have Skills:

1. In-depth AWS ecosystem knowledge: Bedrock, SageMaker data services, Lambda, Batch — beyond the core services.
2. MS Fabric experience — explicitly listed in the Fieldglass posting as a preferred qualification.
3. Databricks experience — active tooling evaluation makes this a near-term differentiator.
4. Spark knowledge — relevant to pipeline and data transformation requirements.
5. Graph database experience (MongoDB, DynamoDB, or graph DB exposure).
6. Experience working alongside ML practitioners in an agile pod environment — understanding what Data Scientists and ML Developers need from data infrastructure.
7. BSc in Computer Science, Engineering, Math, Physics, Statistics, or equivalent.

Job Location:

• Downtown Calgary, Alberta
• Hybrid Work Schedule (Mon/Tue/Thu in office)

WHAT SUCCESS LOOKS LIKE

• Someone with strong data infrastructure focus; 5-8 years total experience, 3+ years in production data engineering.
• Probably comes from an energy, industrial, financial services background with complex multi-source data landscapes and production engineering standards.
• Someone with Python (expert), SQL (performance tuning), AWS production experience, CI/CD, automated testing, ETL/ELT pipeline delivery.
• Someone who is Infrastructure-minded and engineering-disciplined; builds with scale and maintainability in mind; holds the line on code quality while remaining collaborative with ML practitioners.