Data Engineer

• Data Pipeline Development: Design, build, and maintain scalable ETL / ELT pipelines to ingest, transform, and deliver data from multiple sources
• Data Architecture: Develop and optimize data models, schemas, and warehouse structures to support analytics, reporting, and business intelligence needs
• Cloud Data Platforms: Work within cloud environments such as AWS, Azure, or GCP to deploy and manage data solutions
• Data Warehousing: Design and support enterprise data warehouses using platforms such as Snowflake, Redshift, BigQuery, or Azure Synapse
• Big Data Processing: Develop solutions using big data technologies such as Spark, Databricks, Kafka, and Hadoop when required
• Performance Optimization: Tune queries, pipelines, and storage solutions for performance, scalability, and cost efficiency
• Data Quality & Reliability: Implement monitoring, validation, and alerting processes to ensure data accuracy, integrity, and availability
• Collaboration: Work closely with Data Analysts, Data Scientists, Software Engineers, and business stakeholders to understand requirements and deliver data solutions
• Documentation: Maintain detailed documentation for pipelines, data flows, and system architecture

• Proven experience as a Data Engineer or in a similar role
• Strong proficiency in SQL and Python (or similar languages)
• Experience with cloud platforms (AWS, Azure, GCP)
• Hands-on experience with ETL / ELT tools (Airflow, dbt, Fivetran, Matillion, Glue, ADF, etc.)
• Experience with data warehousing platforms such as Snowflake, Redshift, BigQuery, or Azure Synapse
• Strong understanding of data modeling, performance tuning, and pipeline optimization
• Experience with version control systems (Git) and agile development practices
• Excellent problem-solving, analytical, and communication skills

Familiarity with streaming and big data tools (Kafka, Spark, Databricks, Hadoop) is a plus

• medical, vision, dental, and life and disability insurance
• eligible to enroll in our company 401(k) plan