Arcurve is one of North America’s leading full-service technology, advisory and software development companies. In 2006, we began with a belief that there was a better way to deliver skilled services in the technology industry. Since then, we have completed more than 1000 projects for clients ranging from start-ups to Fortune 500 companies.

From our office in Calgary and hubs in Halifax, Houston, and Vancouver we deliver exceptional results for our clients in a diverse range of industries including telecommunications, oil and gas, transportation, private equity, gaming, infrastructure, software, finance, and hospitality.

At Arcurve, we believe that work should be an enjoyable experience and that the best “aha” moments come through team learning and continuous motivation. We know the key to success is collaboration, and that you can’t put a value on accountable, transparent, and authentic interactions. We strive to deliver exceptional service while creating lasting relationships with our employees, our students, our clients, and our community.

We’re looking for an authentic, collaborative, and accountable Data Engineer to join the Arcurve team.

YOU ARE

- Passionate about technology
- An authentic and creative human
- Driven to succeed
- A believer in the importance of teamwork
- Community-minded
- An expert problem solver
- Someone who thrives on challenge
- Motivated by exceptional results
- Someone who cares about your clients

THE GOAL To deliver best-in-class technical solutions across a broad array of clients in different industries utilizing the tech stack best suited to solving the problem with a focus on delivering business value for our clients.

THE ROLE

Arcurve delivers applied machine learning for clients operating in complex technical environments. The value of that work depends on whether it runs reliably against real operational data, which is rarely clean,



complete, or timely.

As a Data Engineer, you will build and operate the pipelines and platform that carry data from source to model to decision, across batch and streaming workloads. You will also take a hands-on role in productionizing machine learning models, working alongside a Data Scientist who owns the analytical design.

This position suits an engineer who thinks in terms of failure modes and who expects their systems to be inherited, debugged, and extended by other people.

THE RESPONSIBILITIES

- Design, build, and maintain production data pipelines, including ingestion, transformation, orchestration, and data quality validation.
- Develop streaming and near-real-time pipelines where operational decisions depend on low latency.
- Own MLOps infrastructure, including CI/CD for models, experiment tracking, model registry, deployment, monitoring, drift detection, and retraining.
- Implement validated machine learning approaches as production services that perform reliably under enterprise load.
- Build data models and semantic layers that support both analyst querying and reliable reasoning by large language models.
- Provision and maintain cloud infrastructure, and establish the observability, alerting, and recovery procedures that keep it dependable.
- Collaborate with data scientists, client engineering teams, and business stakeholders on requirements and delivery.

THE REQUIREMENTS

- Bachelor's degree in Computer Science, Engineering, or a related technical field,



or equivalent practical experience.
- Demonstrated experience building and operating production data platforms.
- Expert-level SQL and strong Python.
- Deep experience with Databricks and/or Snowflake. Experience with BigQuery and Microsoft Fabric is also highly valued.
- Spark, including performance tuning on production workloads.
- Pipeline orchestration and workflow tooling such as Airflow, Databricks Workflows, Azure Data Factory, or dbt.
- MLOps tooling such as MLflow, Databricks Asset Bundles, or Azure ML, with practical experience deploying and monitoring models.
- Experience delivering in a major cloud environment, with Azure preferred and AWS a strong second.
- Working proficiency with Docker and Kubernetes, including containerizing and deploying services.
- CI/CD and infrastructure-as-code practice.
- Data modelling experience across dimensional, normalized, or graph approaches, with the judgment to select appropriately.
- Established software engineering habits, including version control, code review, automated testing, structured logging, and error handling.
- Excellent written and verbal communication with both technical and non-technical audiences.

PREFERRED QUALIFICATIONS

- Streaming platforms such as Kafka, Event Hubs, Kinesis, or Spark Structured Streaming.
- Deploying machine learning models at scale, including real-time inference and GPU workloads.
- Graph databases and graph data modelling.
- Handling unstructured data at volume, including images, documents, and audio.
- Data governance, lineage, and cataloguing.
- Domain exposure to industrial, energy, or engineering-led sectors.

THE PERKS

- A fun work atmosphere that values equity, diversity and inclusion.
- Competitive contractor rates.
- Hybrid work environment and flexible scheduling.
- Contract or Employment opportunities

📌 Intermediate Data Engineer (Calgary)
🏢 Arcurve
📍 Calgary