Data Engineering Scientist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineering Scientist based in Canada.

As a Data Engineering Scientist, you will work at the intersection of data engineering, artificial intelligence, and industrial operations. You will collaborate with data scientists and engineering, manufacturing, and operations experts to transform complex industrial data into reliable inputs for ML-Ops and AI solutions. Your work will span operational technology, industrial sensors, historians, time-series data, video, and audio streams. You will design, experiment with, and optimize data pipelines that support real-world engineering and manufacturing use cases. The role combines hands-on technical work with agile experimentation, data quality management, and advanced analytics. You will help turn industrial data into actionable insights that enable better automation, optimization, and decision-making.

• Collaborate closely with data scientists and engineering, manufacturing, and operations subject matter experts to design data pipelines that effectively support machine learning, deep learning, and advanced analytics use cases.
• Work with AVEVA PI AF, PI EF, and related PI tools to access, structure, transform, and prepare industrial data for reliable downstream analysis and ML-Ops applications.
• Ensure data engineering processes remain rigorous while supporting an agile approach to data validation, merging, transformation, and preparation based on the business use case being addressed.
• Define and monitor business-oriented data quality metrics, identify areas for optimization, and ensure that datasets remain reliable and fit for purpose.
• Analyze data cleanliness and identify potential dataset biases, gaps, or inconsistencies that could affect analytical or machine learning outcomes.
• Experiment with different data pipelines and processing approaches, building and optimizing solutions that extract maximum value from complex industrial datasets.
• Apply data mining techniques and state-of-the-art analytical methods to uncover meaningful patterns, correlations, and insights that can support data scientists and subject matter experts.
• Augment datasets through algorithmic methods or relevant third-party data sources when additional information is needed to strengthen engineering and manufacturing applications.
• Improve data collection procedures by identifying and incorporating information that can contribute to more effective automation, optimization, and operational decision-making systems.
• Work with data engineering specialists to ensure that data processing, cleansing, validation, and integrity checks are performed reliably and that ML-Ops data remains available for 24/7 operations.
• Present early data findings, mining results, and analytical insights clearly to technical and non-technical stakeholders.
• Collaborate with 24/7/365 ML-Ops teams to monitor the data feeding AI and machine learning models and help track data-related impacts on model performance over time.
• Integrate industrial time-series data with geospatial tools to improve data visibility and support more comprehensive industrial analysis.

Requirements

• Hold a Bachelor’s, Master’s, or PhD in a relevant technical, scientific, engineering, data, or related discipline.
• Demonstrate strong knowledge of data engineering, data mining, and statistical modeling, with the ability to apply these disciplines to practical business and industrial problems.
• Have hands-on experience with common data engineering and industrial data platforms, with experience in OSIsoft/AVEVA PI considered essential, as well as exposure to platforms such as Insights Hub, Azure IoT, or AWS IIoT/SiteWise.
• Bring experience working with time-series databases, particularly where industrial sensor data, operational technology, or engineering datasets are involved.
• Possess strong scripting capabilities, typically using Python, SQL, and/or PowerShell, and be comfortable developing practical data processing and analysis solutions.
• Have experience with cloud-hosted data solutions and platforms, including environments such as AWS or comparable industrial cloud technologies.
• Demonstrate experience with data visualization tools and the ability to communicate analytical findings through clear and meaningful visualizations.
• Have strong SQL and query-language skills, together with experience working with NoSQL databases and diverse data structures.
• Experience with geospatial technologies such as Esri ArcGIS is considered an advantage, particularly when combined with industrial or time-series data.
• Bring strong communication skills and the ability to explain data findings, patterns, and technical concepts clearly to both technical specialists and business stakeholders.
• Demonstrate curiosity, analytical thinking, adaptability, and a collaborative approach to solving complex engineering and manufacturing challenges.

Benefits

• Permanent employment with a competitive base salary.
• 100% employer-paid benefits from the first day, including medical, dental, vision, life, and short- and long-term disability insurance.
• Group RRSP/DPSP retirement savings plan with employer contributions, available from the first day.
• Flexible career paths and opportunities for internal career growth and advancement.
• Learning and development opportunities, with the chance to build expertise alongside experienced professionals in industrial technology, data, AI, and engineering.
• Flexible PTO policy, along with sick and personal days and a summer flex schedule designed to support work-life balance.
• Structured onboarding program with dedicated team support and resources from the beginning of employment.
• Opportunity to work on advanced industrial AI, machine learning, IoT, and data engineering challenges with real-world engineering and manufacturing applications.
• An inclusive and diverse workplace committed to equal opportunity and providing reasonable accommodations throughout the hiring and selection process.