Data Engineer in Calgary , Alberta, North America

Airswift is seeking a Data Engineer to work a 12-month contract with one of our major clients in Calgary, AB.

Key Responsibilities

• Design, build, and optimize ETL/ELT workflows using KNIME Analytics Platform

• Integrate data from relational databases, APIs, and cloud storage

• Automate and productionize workflows using KNIME Business Hub

• Develop and scale data pipelines using Databricks (Apache Spark)

• Implement data quality checks, anonymization, and governance controls

• Document workflows, data pipelines, and operational processes

• Lead the design and delivery of scalable Databricks data pipelines using Spark, Delta Lake, and Py

Spark

• Drive Lakehouse architecture across ingestion, transformation, and curated data layers

• Define technical standards and best practices for batch and streaming data engineering

• Optimize performance and cost efficiency of Spark workloads

• Implement enterprise-grade data governance, security, and monitoring (e.g., access controls, catalogs)

• Act as a senior technical leader and mentor within the data engineering team

What You Bring

• Post-secondary degree in Computer Science, Software Engineering, or equivalent experience

• 7+ years of hands-on experience in data engineering

• Strong experience with Databricks, Spark, and Lakehouse architectures

• Advanced proficiency in Py

Spark, Python, and SQL

• Advanced experience with KNIME Analytics Platform (nodes, components, workflow control)

• Solid understanding of data modeling and ETL/ELT best practices

• Experience building and supporting production-grade data pipelines at scale

• Exposure to cloud platforms (Azure , AWS acceptable)

• Familiarity with Dev

Ops / CI/CD practices for data workloads

Nice to Have

• Experience with MLOps and deploying machine learning workloads in Databricks

• Familiarity with data integration tools (e.g., Azure Data Factory, HVR, or similar)

• Experience working in regulated or large enterprise environments

• Exposure to streaming data architectures (Kafka, Structured Streaming)

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