Data Engineer

Blackline Safety

3d•Hybrid

About The Position

Our team at Blackline Safety is growing! As a people-driven technology company, with a mission to make sure every worker returns home safely, we drive innovation, practice resiliency, demonstrate leadership, go the extra mile for our customers, and empower our people to be their best. As a Data Engineer, you’ll help expand, optimize, and maintain data pipelines and infrastructure that power our IoT-enabled safety ecosystem. You’ll work with large, real-time and batch datasets from devices across the globe — ensuring our systems are scalable, secure, and delivering accurate, timely data to the teams who need it.

Requirements

Degree in Computing Science, Data Engineering, Information Technology, or related discipline.
3+ years of experience in a Data Engineering role.
Experience with event-driven architecture and related technologies (e.g., Apache Kafka, Amazon MSK).
Familiarity with Infrastructure as Code (IaC) and CI/CD best practices in multi-environment deployment workflows, including hands-on experience with 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 Haves

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.