Data Engineer — Real-Time IoT Data Pipelines (Hybrid)

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Calgary

Hybrid

CAD 100,000 - 140,000

Full time

14 days+

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Benefits offered by this job

Health benefits

Mental health support

Hybrid work model

Vacation and leave

Job summary

Blackline Safety is seeking a Data Engineer to expand, optimize, and maintain data pipelines and infrastructure powering its IoT-enabled safety ecosystem. You will work with real-time and batch datasets from devices worldwide, ensuring scalable, secure, and timely data delivery to analytics and product teams.

The role requires 3+ years in data engineering, strong SQL, Python/Java/Scala, and hands-on AWS experience (Redshift, EMR, S3, Lambda, Kinesis, MSK).

Qualifications

3+ years of experience in a Data Engineering role.
Experience with event-driven architecture and related technologies.
Familiarity with Infrastructure as Code (IaC) and CI/CD best practices in multi-environment deployment workflows, including Terraform and GitHub Actions.
Experience with monitoring and alerting platforms such as CloudWatch, Datadog, or PagerDuty.
Hands-on experience with ETL/ELT and orchestration tools including AWS Glue Studio, Matillion, and AWS Step Functions.
Proficiency with AWS data services: Redshift, EMR, S3, Lambda, Kinesis, and MSK.
Strong proficiency in SQL, Python, Java, and/or Scala.
You’re a pragmatic builder and problem-solver who’s passionate about enabling better decisions through data.
You understand that clean architecture and thorough documentation are essential, not optional.
You work well in agile teams and enjoy collaborating with product, engineering, and analytics partners.
You have opinions on data modeling, enjoy the challenge of working with event-driven architectures, and are comfortable switching between SQL, Python, Java, Scala and a variety of AWS services.
You have embraced AI-assisted development as a core part of your workflow - leveraging agentic tools such as Claude Code, Amazon Q Developer, and GitHub Copilot to accelerate delivery, reduce toil, and maintain higher standards of code quality.
Nice to Have
Experience with additional big data technologies such as BigQuery, Snowflake, Microsoft Fabric, Hive, Hadoop, Apache Flink, or Apache Spark.
Familiarity with BI and analytics tools including Power BI and Amazon Quick.
Experience supporting data science teams with the infrastructure, tooling, and model-serving pipelines they need.
Familiarity with AI/ML services on AWS, including Amazon Bedrock, SageMaker, or Strands, and an understanding of how data engineering work underpins model training and inference workflows.
Comfort working alongside data scientists to help productionize models, ensure training data quality, and build reliable data feeds for analytical and predictive use cases.

Responsibilities

Design, build, and maintain scalable data pipelines (batch and real-time) using modern cloud tools.
Partner closely with product and engineering teams to deliver data-driven features and services that support our safety mission.
Support our existing ETL/ELT processes, data lake, data warehouse, and data lakehouse environments.
Ensure data quality, reliability, and observability through robust monitoring, alerting, and documentation practices.
Participate actively in architectural discussions and contribute to the long-term strategy of our data platform.