Senior Data Engineer – Data Analytics & BI
New
J
JobgetherData analytics
CanadaFull-TimeSenior
SalaryUp to 135,000 CAD per year
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Job Details
- Experience
- 8+ years of professional experience with SQL; 5+ years of hands-on Python experience; 8+ years of experience developing custom reports, dashboards, and visualizations using Tableau or comparable business intelligence tools.
- Required Skills
- AWSPythonSQLApache AirflowGCPTableauAzureData visualizationdbt
Requirements
- Have 8+ years of professional SQL experience, including complex query optimization and large-scale relational and columnar databases.
- Have 5+ years of hands-on Python experience focused on data engineering, pipeline development, automation, and data transformation.
- Have 8+ years developing custom reports, dashboards, and visualizations using Tableau or comparable BI tools.
- Bring experience validating, auditing, and reconciling data and reports across multiple systems.
- Have hands-on experience with cloud data platforms such as AWS, Azure, or Google Cloud Platform, and distributed technologies such as Spark or Hadoop.
- Know relational database systems and have familiarity with NoSQL technologies such as MongoDB or Cassandra.
- Have experience with data orchestration and transformation tools such as Apache Airflow and dbt.
- Have experience with Apache Hive and Presto for distributed query processing.
- Have experience supporting large-scale data migrations, from requirements gathering and pipeline design through implementation and operational support.
- Bring familiarity with machine learning concepts, algorithms, and data science workflows, and an understanding of DevOps principles for data workflows.
- Have experience mentoring or coaching junior engineers and analysts.
- Have a bachelor's or master's degree in a listed data-relevant discipline, or equivalent professional experience.
- Be able to explain technical concepts to non-technical audiences, collaborate with stakeholders, and create technical documentation such as design specifications, runbooks, and data dictionaries.
- Be comfortable working in ambiguous, fast-paced environments, shifting between strategic initiatives and tactical analytics requests, and working with distributed teams using remote collaboration tools.
- Apply thorough testing, clear documentation, and maintainable engineering practices.
Responsibilities
- Triage people-data, reporting, and ad hoc analysis requests, prioritize work, and communicate delivery expectations to stakeholders.
- Design, develop, automate, and maintain scalable ETL/ELT pipelines for data from internal and external sources.
- Build self-service reports, dashboards, and visualizations that communicate KPIs and support decision-making.
- Partner with stakeholders to define reporting requirements and improve dashboards, analytics solutions, and data products.
- Use SQL, Python, Tableau, and Jupyter Notebook to extract, organize, analyze, and visualize data.
- Collaborate with Data Engineering, Data Science, and cross-functional teams on data collection, pipeline architecture, and analytics solutions.
- Audit data integrity, investigate data-quality issues, and establish processes to improve reporting accuracy and reliability.
- Optimize ETL processes through architecture, code, and performance improvements.
- Guide end users in interpreting metrics, using dashboards, and applying data effectively.
- Document reporting requirements, technical designs, data definitions, operational procedures, and changes to analytics solutions.
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