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Data Engineer
JOB_REQUIREMENTS
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Key Responsibilities:
Preferred Qualifications:
Data Pipeline Development & Integration
Design, implement, and maintain automated data pipelines to ingest, transform, and deliver data across multiple systems.
Build and manage data connectors using tools such as Fivetran or custom integrations to ensure seamless data flow from source systems (e.g., CRM, ERP, application data bases).
Data Warehouse Management
Maintain and optimize our Amazon Redshift data warehouse to ensure high performance, scalability, and cost efficiency.
Monitor, troubleshoot, and improve query performance and warehouse utilization.
Implement data partitioning, clustering, compression, and other optimization strategies.
Data Quality & Governance
Establish and enforce best practices for data modeling, transformation, and validation.
Ensure data accuracy, consistency, and timeliness across all downstream systems.
Collaborate with stakeholders to define data requirements and establish a single source of truth .
Collaboration & Stakeholder Support
Partner with analysts, BI developers, and data scientists to deliver reliable data sets for reporting and advanced analytics.
Work with cross-functional teams to identify and prioritize data engineer ing initiatives.
Document data pipelines, models, and workflows to support transparency and maintainability.
Continuous Improvement
Evaluate and implement new tools, frameworks, and technologies to improve efficiency and scalability.
Drive automation and innovation in data engineer ing practices.
Preferred Qualifications:
Experience with Amazon Redshift or other cloud data warehouses.
Hands-on experience with Fivetran, dbt, Airflow , or similar ETL/ELT tools.
Strong SQL skills and familiarity with query optimization techniques.
Proficiency in Python or another programming language for data engineer ing.
Familiarity with data modeling concepts (Kimball, Data Vault, star/snowflake schemas).
Experience working in a collaborative, fast-paced environment.- Comfortable helping inform decisions about new pipeline architecture - and should not shy away from breaking new ground in our data maturation. This includes being comfortable with change and building something from nothing.
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