Data Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Assume, assess, and take complete ownership of existing ETL/ELT pipelines — determining what should be retained, enhanced, or replaced based on performance, reliability, and long-term maintainability.

• Design and construct scalable improvements or new solutions where existing practices are inadequate.

• Monitor the health of pipelines, troubleshoot data quality issues, and proactively address performance and reliability challenges.

• Manage and advance orchestration tools, remaining open to adopting superior alternatives as infrastructure requirements expand.

• Enhance query performance, pipeline efficiency, and resource utilization throughout Convo’s data ecosystem.

• Engage in testing, deployment, and monitoring practices that foster long-term reliability and scalability.

• Develop and sustain scalable data transformation processes, schema design, and data models that adapt to evolving business needs.

• Establish and refine data quality testing frameworks — creating practices that identify issues early and foster enduring trust in our data internally.

• Oversee data governance, documentation, lineage, version control, and data quality standards across the organization.

• Act as the primary internal resource for data engineering guidance and recommendations, helping to set standards and inform data infrastructure decisions throughout the organization.

• Collaborate closely with data analysts to translate business inquiries into reliable, queryable data structures.

• Educate and assist non-technical stakeholders in effectively working with data, understanding feasibility, and articulating data requests clearly.

• Investigate and implement tools to facilitate self-service data discovery for internal teams, reducing bottlenecks and empowering stakeholders to independently find answers.

• Work alongside Product, Engineering, Finance, Operations, and Data Science stakeholders to meet reporting, forecasting, and business intelligence requirements.

• Collaborate with Product and Engineering teams to integrate analytics, event tracking, and reporting into products and platforms.

• Establish and document data engineering standards, workflows, and best practices at Convo — creating a foundation that is sustainable, well-understood, and not reliant on any single individual.

• Contribute to enhancements in data architecture, tooling, monitoring, automation, and engineering best practices.

• Assess emerging technologies and tools to boost efficiency, automation, and data systems accessibility.

• Maintain clear technical documentation and operational standards that support long-term maintainability.

• Apply sound technical judgment in balancing immediate business needs with the long-term sustainability of the platform.

• Uphold strong confidentiality and discretion when managing sensitive organizational, financial, operational, and employee data.

⛳️ Requirements

• Strong SQL capabilities with practical experience in Snowflake and Snowflake SQL.

• Proficiency in Python for data transformation, automation, and pipeline scripting.

• Experience with dbt for data modeling and transformation.

• Familiarity with git and best practices in version control.

• Solid understanding of ETL/ELT patterns, pipeline orchestration, and contemporary data modeling concepts.

• Experience in managing and supporting production-grade data infrastructure and pipelines.

• Proven ability to work independently, prioritize effectively, and make sound technical decisions without daily oversight.

• Experience in troubleshooting data quality, reliability, and performance issues in complex data environments.

• Ability to clearly communicate technical concepts and guide non-technical stakeholders regarding data capabilities and limitations.

• A collaborative attitude and comfort working across teams with diverse technical backgrounds.

• Willingness to take over existing systems and the discernment to know when to enhance versus rebuild.

• Openness to learning new tools and technologies as the data engineering landscape evolves.

• Strong integrity and professionalism in handling sensitive and confidential information.

🏝️ Benefits

• Health insurance

• 401(k) matching

• Flexible work hours

• Paid time off