Intermediate Data Engineer

Arcurve; Calgary; Hybrid; CAD 90,000 - 140,000; Full time

Benefits offered by this job

Hybrid work environment

Flexible scheduling

Contract or Employment opportunities

Competitive rates

Job summary

Arcurve is seeking a Data Engineer to design and operate data pipelines across batch and streaming workloads, ensuring data quality from source to model to decision. You will productionize ML models with a Data Scientist and maintain robust MLOps in a cloud environment.

The role emphasizes building scalable pipelines, low-latency streaming, and collaboration with client teams. A strong foundation in SQL, Python, and cloud tooling is essential.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field, or equivalent practical experience.
• Experience building and operating production data platforms.
• Strong SQL and Python skills.
• Deep experience with Databricks and/or Snowflake; BigQuery and Microsoft Fabric valued.
• Spark experience with production workloads.
• Pipeline orchestration tools like Airflow, Databricks Workflows, Azure Data Factory, or dbt.
• MLOps tooling with MLflow, Databricks Asset Bundles, or Azure ML; deployment/monitoring experience.
• Cloud experience (Azure preferred; AWS strong second).
• Docker and Kubernetes for containerized services; CI/CD practices.

Responsibilities

• Design, build, and maintain production data pipelines including ingestion, transformation, orchestration, and data quality validation.
• Develop streaming and near-real-time pipelines with low latency for operational decisions.
• Own MLOps infrastructure: CI/CD for models, experiment tracking, model registry, deployment, monitoring, drift detection, retraining.
• Implement production-ready ML approaches as services that scale under enterprise load.
• Build data models and semantic layers to support analytics and large language models.
• Provision and maintain cloud infrastructure with observability, alerting, and recovery procedures.
• Collaborate with data scientists, client engineering teams, and business stakeholders to gather requirements and deliver solutions.

Skills

SQL

Python

Databricks

Snowflake

BigQuery

Spark

Airflow

CI/CD

Docker

Kubernetes

DBT

Azure Data Factory

Databricks Workflows

Azure ML

MLflow

Terraform

Version control

Education

Bachelor's degree in Computer Science/Engineering

Tools

Databricks

Snowflake

BigQuery

Job description

We’re looking for an authentic, collaborative, and accountableData Engineerto 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