Senior Data Developer, Analytics & Insights
New
CanadaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonSQLETLHadoopSnowflakeAirflowSparkTerraformData modeling
Requirements
- 5+ years of experience in data engineering or large-scale data processing within cloud-based environments, with strong AWS expertise.
- Strong programming skills in Python and advanced proficiency in SQL.
- Experience with data modeling, analytical data warehouses (e.g., Snowflake, Presto, Hive), and dimensional modeling techniques.
- Hands-on experience with data pipeline orchestration tools such as Airflow.
- Strong understanding of ETL/ELT processes and experience working with both traditional and modern data engineering frameworks.
- Familiarity with big data technologies such as Spark and Hadoop.
- Experience handling semi-structured data formats such as JSON and Parquet.
- Knowledge of AWS services (e.g., Glue, Lambda, EMR, EKS) and cloud-based data architecture.
- Exposure to infrastructure-as-code and automation tools such as Terraform, Git, and Jenkins is an asset.
- Strong communication, collaboration, and problem-solving skills in a fast-paced environment.
Responsibilities
- Design, develop, automate, and maintain scalable ELT/ETL data pipelines that process large volumes of structured and unstructured data from multiple sources.
- Improve and maintain existing data architecture to ensure reliable, efficient, and secure data flow across platforms.
- Collaborate with data peers, product managers, and cross-functional stakeholders to gather requirements, define solutions, and document technical designs.
- Implement best practices for data quality, governance, monitoring, validation, and auditing to ensure trustworthy datasets.
- Optimize pipeline performance and resource efficiency using modern engineering and AI-native approaches, including anomaly detection and schema evolution.
- Work with big data technologies and frameworks to support large-scale analytical workloads.
- Contribute to continuous innovation by adopting emerging tools, technologies, and industry best practices in data engineering.
- Support integration of data pipelines with cloud infrastructure and analytics platforms to enable downstream insights and reporting.
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