Senior Data Engineer - ETL (Python + Snowflake)
New
B
BluelightData engineering
BelizeFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLGitMicrosoft AzureSnowflakeCI/CDAzure DevOpsPySpark
Requirements
- Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a related field, or equivalent professional experience.
- Proven experience developing end-to-end data pipelines that extract, transform, and load data from REST APIs, relational databases, cloud storage, and flat files.
- Hands-on experience with Snowflake virtual warehouses, streams, tasks, stages, Snowpipe, secure data sharing, and performance optimization.
- Advanced SQL development skills, including complex queries, performance tuning, and optimization of large-scale workloads.
- Experience with dimensional modeling techniques, including star schemas, fact tables, and dimension tables.
- Familiarity with cloud-based data ecosystems, particularly Microsoft Azure.
- Experience managing source code and CI/CD pipelines using Git and Azure DevOps or similar.
- Knowledge of data integration best practices, data governance, and enterprise data management.
- SnowPro, Azure Data Engineer Associate, or equivalent cloud data platform certification is preferred.
- Experience with big data technologies, machine learning, data science platforms, advanced analytics, BI tools, Agile delivery frameworks, or DevOps practices is preferred.
Responsibilities
- Design, develop, and maintain scalable ETL/ELT pipelines using Python (PySpark), Snowflake, and cloud-native technologies.
- Build reusable data ingestion, transformation, and loading processes.
- Implement and manage Snowflake databases, schemas, tables, views, streams, tasks, stages, and stored procedures.
- Optimize Snowflake warehouses, data sharing, automated scaling, and SQL workloads for performance and cost efficiency.
- Extract and ingest structured and semi-structured data from REST APIs, relational databases, SaaS apps, flat files, and cloud storage.
- Develop ingestion frameworks and collaborate with data architects and stakeholders on logical and physical data models.
- Implement data quality controls, validation frameworks, monitoring, governance, and security practices.
- Monitor pipelines, diagnose performance issues, support production environments and incident resolution, and document operational processes.
- Partner with data architects, data scientists, analysts, and business stakeholders to understand requirements and provide technical expertise.
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