Data Engineering Specialist

at  Maya HTT

Maya HTT is a world leading developer of digital industries software solutions. The world’s top tier engineering and manufacturing organizations rely on MAYA’s experience and expertise to achieve the full potential of their software investment. MAYA HTT is at the forefront of innovation technology. Our team delivers extensive engineering and CAE simulation expertise along with cutting-edge digitalization solutions, such as AI, machine learning, IIoT, and Industry 4.0.
As a data engineering specialist, you’ll work closely with a team of data scientist and engineering/manufacturing/operations subject matter experts, mostly with data coming from operational technologies (OT), historians, industrial sensor data, video streams data, and audio stream data. You will also work with engineering and manufacturing experts in order to solve industrial operations business use cases by creating data pipelines to feed ML-Ops systems running machine learning, deep learning and advanced analytics in general. You will be called upon to align the data available, how to access it securely, transform it, merge and fuse it with other data streams, in order to pre-process the right data and at the right frequency, and make that data reliably useable by the right AI technology(ies). Your focus as part of the team is to select the appropriate data pipelines to best solve the engineering and manufacturing challenges posed collaboratively by our clients and our own Maya engineering and manufacturing experts. You will collaborate with other team members and architect the right data pipelines which will help create a practical AI-Ops (or ML-Ops) solutions for effective decision making by Maya HTT’s clients. As is often the case in newer engineering and manufacturing applications leveraging AI technologies, you will work in an agile fashion to build data pipelines and design experiments to extract the value within the data provided. Given Maya HTT’s focus in engineering and manufacturing AI applications, excellent understanding of time-series from industrial sensors is a big plus, and video processing and audio data processing is a plus.

What to expect as your main responsibilities:

• Collaborating with data scientist to create the right data pipelines
• Ensuring the data engineering is done properly while remaining agile in the gradual data validation, data merging, and in general produce the right data aligned to the business use case to solve
• Defining the data quality metrics to be tracked from a business perspective, and metrics to be optimized on
• Checking data cleanliness and identifying dataset biases whenever relevant
• Experimenting, building, and optimizing selected data pipeline and methods
• Leveraging data mining to uncover interesting patterns or correlation using state-of-the-art methods to help data scientists and subject matter experts
• Augmenting datasets either algorithmically or using third party sources of information when needed
• Enhancing data collection procedures to include information that is relevant for building better engineering and manufacturing automation & optimization systems
• Interacting with data engineering specialist to ensure data processing, cleansing, and verifying the integrity of data used for the ML-Ops is reliably done and available during 24/7 operations
• Presenting data mining and early data finding results in a clear manner
• Collaborating with 24/7/365 ML-Ops team and ensuring the tracking of data feeding the ML/AI model performance overtime

Minimum Requirements:

• A Bachelor, Master degree or PhD
• Excellent understanding of data engineering, data mining, and statistical modeling
• Experience with common data engineering toolkits such as ex-OSIsoft Aveva PI (a must have), or MindSphere/Insights Hub, or Azure IoT
• Experience with timeseries database is a big plus, especially if you have hands-on experience with industrial sensors data
• Excellent scripting skills (typically python, SQL)
• Good data story communication skills is a big plus
• Experience with data visualisation tools
• Experience using query languages such as SQL
• Experience with NoSQL databases
• Good teamwork skills

Why Maya HTT?

• Flex Working Hours and Hybrid Work. Office downtown Montreal, 2-minute walk from Atwater Metro.
• Permanent Position, Competitive Base Salary, Yearly Increase and Bonus.
• 100% Employer-Paid Benefits starting from Day One: medical, dental, life, short/long term disability insurances.
• Retirement Savings: Group RRSP / DPSP Plan with Employer Contributions open to join from Day One.
• Career Growth Opportunities: Our flexible career paths allow you to grow, and we like to promote internally.
• Learning Opportunities: Learn from the best in the industry and develop your skills.
• Generous Time-Off Policy: We promote a healthy work-life balance with an excellent and flexible PTO Policy.
• Structured Onboarding Program: We’re invested in your success; you’ll have team members to support you and provide a wide range of assistance from Day One.
• Join an award-winning company that is recognized worldwide as an industry leader.

Our Candidate Experience Flow: HR Phone Screen - Virtual Interviews using Microsoft Teams - Job Offer
Maya HTT is an equal opportunity employer and committed to fostering diversity and inclusion in the workplace. Accommodations are available upon request for candidates taking part in all aspects of the hiring and selection process

• Collaborating with data scientist to create the right data pipelines
• Ensuring the data engineering is done properly while remaining agile in the gradual data validation, data merging, and in general produce the right data aligned to the business use case to solve
• Defining the data quality metrics to be tracked from a business perspective, and metrics to be optimized on
• Checking data cleanliness and identifying dataset biases whenever relevant
• Experimenting, building, and optimizing selected data pipeline and methods
• Leveraging data mining to uncover interesting patterns or correlation using state-of-the-art methods to help data scientists and subject matter experts
• Augmenting datasets either algorithmically or using third party sources of information when needed
• Enhancing data collection procedures to include information that is relevant for building better engineering and manufacturing automation & optimization systems
• Interacting with data engineering specialist to ensure data processing, cleansing, and verifying the integrity of data used for the ML-Ops is reliably done and available during 24/7 operations
• Presenting data mining and early data finding results in a clear manner
• Collaborating with 24/7/365 ML-Ops team and ensuring the tracking of data feeding the ML/AI model performance overtim