Data Engineer: Python, Snowflake
5 days ago
Duties & Responsibilities
- Design, develop, and maintain scalable ETL/ELT data pipelines to support business and analytics needs
- Write, tune, and optimize complex SQL queries for data transformation, aggregation, and analysis
- Translate business requirements into well-designed, documented, and reusable data solutions
- Partner with analysts, data scientists, and stakeholders to deliver accurate, timely, and trusted datasets
- Automate data workflows using orchestration/scheduling tools (Airflow, ADF, Luigi, etc.)
- Develop unit tests, integration tests, and validation checks to ensure data accuracy and pipeline reliability
- Document pipelines, workflows, and design decisions for knowledge sharing and operational continuity
- Apply coding standards, version control practices, and peer code reviews to maintain high-quality deliverables
- Proactively troubleshoot, optimize, and monitor pipelines for performance, scalability, and cost efficiency
- Support function roll outs, including being available for post-production monitoring and issue resolution
Requirements
Basic Qualifications
- Bachelor's degree in computer science, Information Systems, Engineering, or a related field
- 2–5 years of hands-on experience in data engineering and building data pipelines
- At least 3 years of experience in writing complex SQL queries in a cloud data warehouse/ data lake environment.
- Solid hands-on experience with data warehousing concepts and implementations
- At least 1 year of experience with Snowflake or another modern cloud data warehouse
- At least 1 year of hands-on Python development.
- Familiarity on Data modeling and Data warehousing concepts
- Experience with orchestration tools (e.g., Airflow, ADF, Luigi)
- Familiarity with at least one cloud platform (AWS, Azure, or GCP)
- Strong analytical, problem-solving, and communication skills
- Ability to work both independently and as part of a collaborative team
Preferred Qualifications
- Experience with DBT (Data Build Tool) for data transformations
- Exposure to real-time/streaming platforms (Kafka, Spark Streaming, Flink)
- Familiarity with CI/CD and version control (Git) in data engineering projects
- Exposure to the e-commerce or customer data domain
- Understands the technology landscape, up to date on current technology trends and new technology, brings new ideas to the team
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