Analytics Engineer
Toronto, Ontario, Canada
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Full Time Mid-level / Intermediate USD 80K - 100K
Mars United Commerce
- Core Responsibilities: Data Integration, Data Cleaning, Data Transformation, Data Warehousing, Dashboarding, MLOps, Charts & Visualizations, Reporting Automation, etc.
- Primary Tools: Databricks, Azure Synapse, Power BI
- Develop and maintain data pipelines and ETL processes.
- Optimize data infrastructure for efficient data processing.
- Ensure data quality and accessibility for data scientists and analysts.
- Collaborate with cross-functional teams to address data needs and challenges.
- Implement data governance and security best practices.
- Support annual planning initiatives with clients.
- Work closely with cross-functional teams, including analysts, product managers and domain experts to understand business requirements, formulate problem statements, and deliver relevant data science solutions.
- Develop and optimize machine learning models by processing, analyzing and extracting data from varying internal and external data sources.
- Develop supervised, unsupervised, and semi-supervised machine learning models using state-of-the-art techniques to solve client problems.
- Show up - be accountable, take responsibility, and get back up when you are down.
- Make stuff.
- Share so others can see what’s happening.
- A bachelor’s/master’s degree in Data Analytics, Computer Science, or a directly related field.
- 3-5 years of industry experience in a data analytics or related role.
- Proficiency in SQL for data querying and manipulation.
- Experience with data warehousing solutions.
- Design, implement, and manage ETL workflows to ensure data is accurately and efficiently collected, transformed, and loaded into our data warehouse.
- Proficiency in programming languages such as Python and R.
- Experience with cloud platforms such as AWS, Azure, and Google Cloud.
- Experience in developing and deploying machine learning models.
- Knowledge of machine learning engineering practices, including model versioning, deployment, and monitoring.
- Familiarity with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
- Ability to design and develop scalable data pipelines for batch and real-time data processing.
- Experience with big data technologies such as Apache Spark, Hadoop, or similar.
- Proficiency in working with structured and unstructured data sources.
- Knowledge of data governance and security best practices.
- Strong understanding of data modeling techniques and best practices.
- Experience with DevOps or MLOps practices for continuous integration and deployment.
- Establish and create scalable and intuitive reporting methodologies through Power BI, suggesting the best representation and visualizations.
- Identify business intelligence needs recommending the best KPIs and customer valuation models and dashboards.
- Interpret data, analyze results, and identify trends or patterns in complex data sets.
- Filter and “clean” data and review computer reports, printouts, and performance indicators to locate and correct data corruption problems.
- Data collection, setting and leveraging DMP and CDP-based infrastructures, attribution modeling, A/B & multivariate testing, and dynamic creative.
- Develop, evaluate, test, and maintain architectures and data solutions such as ETL Pipelines, Data Warehouses, Data Marts, etc.
- Automate data pipelines and develop automation workflows.
- Develop scalable and intuitive ETL & ELT pipelines from a variety of marketing sources such as Salesforce, Adobe Analytics, etc.
- Identify data sources and create data pipelines using shell scripts or Python scripts.
- Create technical documentation.
- Plan data analysis work and develop execution estimates, continuously improving the accuracy of the estimates.
- Develop Single Customer View stitching 1P data from various data sources.
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Tags: Architecture AWS Azure Big Data Business Analytics Business Intelligence CAD Computer Science Data analysis Data Analytics Databricks Data governance Data pipelines Data quality Data warehouse Data Warehousing DevOps ELT Engineering ETL GCP Google Cloud Hadoop KPIs Machine Learning ML models MLOps Pipelines Power BI Python PyTorch R Salesforce Scikit-learn Security Spark SQL TensorFlow Testing Unstructured data
Perks/benefits: 401(k) matching Career development Flex hours Flex vacation Health care Medical leave Parental leave Wellness
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