Data Engineer, AssetWatch

• Bachelor’s degree in Computer Science, Information Systems, Data Science, or a related field
• 2–4 years of experience in a data engineering role or similar environment
• Hands-on experience building ETL/ELT jobs with AWS services such as Glue, Lambda, and Step Functions
• Experience with orchestration tools like Airflow
• Strong proficiency in Python and SQL
• Experience working with Redshift Serverless, Aurora MySQL, Timestream, DynamoDB, or similar databases
• Comfortable designing and managing S3-based data lakes with structured zone patterns and datalakes
• Experience implementing infrastructure-as-code using Terraform
• Solid understanding of data modeling principles and performance optimization
• Detail-oriented with a focus on quality, reliability, and maintainability
• Excellent problem-solving abilities, organizational skills, and ability to manage multiple priorities effectively
• Strong communication skills, both written and verbal, facilitating collaboration across diverse technical teams
• Proven experience designing and implementing backend solutions in complex, scalable cloud environments
• A proactive learner, eager to explore new technologies and methodologies
• Comfortable in dynamic, collaborative environments, able to work independently and in teams
• We have a distributed team that works remotely across locations in the United States and Ontario, Canada. Collaboration within core working hours is required

Desirable

• The Data Services team designs, manages, and optimizes critical backend data infrastructure and pipelines supporting AssetWatch’s condition monitoring and predictive maintenance solutions
• Our team maintains and evolves serverless architectures, builds robust APIs, ensures efficient database operations, and drives improvements in data ingestion and alerting systems
• We collaborate closely across multiple engineering teams to enhance system performance, scalability, and reliability, ensuring seamless data flow and availability
• Collaborate closely with the Data Architect and other stakeholders to translate data models and product requirements into scalable data solutions
• Develop and maintain ETL/ELT pipelines that ingest, process, validate, and load data across AWS environments while adding observability to the processes
• Build and manage data workflows using AWS Glue, Lambda, and Step Functions to support real-time and batch processing
• Work within structured S3 Raw, Curated, and Consumption zones, ensuring data quality and consistency
• Participate in establishing data standards, naming conventions, and metadata practices
• Incorporate AI-assisted development tools to enhance data scrubbing, anomaly detection, and transformation logic within ETL/ELT pipelines
• Proactively use AI tools to improve engineering velocity, reduce manual rework, and elevate overall pipeline reliability and maintainability
• Use Terraform to define and manage data infrastructure in a repeatable, auditable manner
• Troubleshoot data issues, performance bottlenecks, and pipeline failures with a proactive mindset
• Implement data validation, monitoring, and quality checks to ensure reliability of pipelines
• Load, maintain, and optimize datasets in Redshift Serverless, Aurora MySQL, DynamoDB, and Timestream
• Write and optimize performant SQL queries, stored procedures, and database schemas using MySQL
• Monitor, manage, and optimize alerting systems including Sentry and Slack integrations to proactively address infrastructure and database health
• Create, manage, and improve infrastructure-as-code (IaC) scripts and Terraform templates
• Work closely with Data Team to follow and mature best data practices including integration pipeline strategy
• Collaborate with Product and Engineering Teams to identify, plan, and implement system. improvements, addressing technical debt and enhancing overall data stack efficiency
• Participate actively in managing and coordinating production deployments and production support
• Conduct thorough code reviews, support and guide Engineering Teams with backend best practices, and maintain comprehensive documentation