Data Engineer
Essential Duties and Responsibilities:
- Engineering & Development
- Build and maintain scalable ETL/ELT pipelines for batch and streaming data.
- Write high-quality SQL and Python for data transformations and integrations.
- Optimize data models, queries, and workflows for performance and cost efficiency.
- Partner with analysts and data scientists to deliver reliable datasets for BI, analytics, and ML.
- Architecture & Infrastructure
- Contribute to the design of modern data warehouse and lakehouse solutions (Snowflake, Redshift, BigQuery, Databricks, etc.).
- Implement data orchestration and transformation frameworks (Airflow, dbt, Prefect).
- Support data infrastructure in the cloud (AWS/GCP/Azure), ensuring scalability, reliability, and security.
- Mentorship & Collaboration
- Provide technical guidance and informal mentoring to less experienced data engineers.
- Participate in code reviews, design discussions, and knowledge-sharing sessions.
- Promote best practices in coding standards, documentation, and testing.
- Quality & Operations
- Establish and maintain standards for data quality, observability, and reliability.
- Troubleshoot pipeline and infrastructure issues in production environments.
- Contribute to automation and monitoring efforts to reduce manual operations.
- Experience
- 4–6+ years of experience in data engineering or related fields.
- Prior experience collaborating with and mentoring junior engineers.
- Technical Skills
- Advanced SQL and query optimization.
- Strong Python for data engineering (Pandas, PySpark, or similar).
- Hands-on experience with data orchestration (Airflow, dbt, Prefect).
- Familiarity with modern cloud-based data platforms (AWS Redshift, Snowflake, BigQuery, Synapse, Databricks).
- Strong understanding of data modeling and schema design.
- Experience with streaming technologies (Kafka, Kinesis, Pub/Sub).
- Familiarity with CI/CD and version control workflows.
- Exposure to data governance and security frameworks.
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