Data Engineering Scientist

Accountabilities: • 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.

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