Data Engineer (Hybrid)
Summary
Data Engineer building and maintaining reliable ETL/ELT pipelines, integrating data from multiple sources into warehouses or data lakes, and ensuring data quality. Core stack includes SQL, Python, orchestration tools like Airflow/dbt/Dagster, and warehouses such as BigQuery, Snowflake, Redshift, or PostgreSQL.
This Data Engineer position is focused on building and maintaining reliable data pipelines, processing and integrating data from multiple sources into data warehouses or data lakes, and ensuring data quality and consistency across systems.
Key Responsibilities
Build and maintain reliable ETL/ELT pipelines
Process and integrate data from multiple sources into data warehouses or data lakes
Write efficient and maintainable SQL queries
Ensure data quality, consistency, and validity across data pipelines
Collaborate with Data Analysts, Data Scientists, Product, and Engineering teams
Monitor, troubleshoot, and optimize pipeline performance.
Requirements
3–5 years of experience as a Data Engineer or in a similar role
Strong SQL skills and a solid understanding of basic data modeling
Hands-on experience using Python for data processing
Experience with ETL/ELT tools or workflow orchestration platforms such as Airflow, dbt, Dagster, or similar tools
Familiarity with data warehouses such as BigQuery, Snowflake, Redshift, or PostgreSQL
Understanding of data quality, partitioning, indexing, and pipeline monitoring.
Nice to Have
Experience with cloud platforms such as GCP, AWS, or Azure
Familiarity with Apache Spark, Apache Kafka, or data streaming
Experience working with analytics teams or production data platforms.
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