Data Engineer at AssetWatch, Inc.

, , United States -

Full Time

Start Date

Immediate

Expiry Date

13 Mar, 26

Salary

0.0

Posted On

13 Dec, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, ETL, AWS, Python, SQL, Terraform, Data Modeling, Data Quality, Database Management, S3, Redshift, Aurora MySQL, DynamoDB, Timestream, AI Tools, Infrastructure-as-Code

Industry

Automation Machinery Manufacturing

Description

AssetWatch serves global manufacturers by powering manufacturing uptime through the delivery of an unparalleled condition monitoring experience, with a passion to care about the assets our customers care for every day. We are a devoted and capable team that includes world-renowned engineers and distinguished business leaders united by a common goal – To build the future of predictive maintenance. As we enter the next phase of rapid growth, we are seeking people to help lead the journey. 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. Qualifications 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. Nice to Have Knowledge of big data ingestion and scalable data ingestion processes Experience working on data ingestion/storage in a start-up environment #LI-REMOTE What We Offer: AssetWatch is a remote-first company that puts people at the center of everything we do. We want our team members to thrive - that’s why we offer a range of benefits and perks designed to support your well-being, growth, and work-life balance. Competitive compensation package including stock options Flexible work schedule Comprehensive benefits including retirement plan match Opportunity to make a real impact every day Work with a dynamic and growing team Unlimited PTO We have a distributed team that works remotely across locations in the United States and Ontario, Canada. Collaboration within core working hours is required.

Responsibilities

The Data Engineer will develop and maintain ETL/ELT pipelines and build data workflows using AWS services. They will also collaborate with various teams to enhance system performance and reliability.