Data Engineer

InVision Communications

Data Engineer supporting and advancing data infrastructure at Convo Communications, enabling operational and strategic decision-making. Collaborating with cross-functional teams to ensure data reliability and accessibility.

Posted 5/30/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $100,000 - $110,000 per yearWebsite

Tech Stack

Tools & technologies

ETLPythonSQL

About the role

Key responsibilities & impact

Inherit, evaluate, and take full ownership of existing ETL/ELT pipelines — identifying what to preserve, improve, or replace based on performance, reliability, and long-term maintainability.
Design and build scalable pipeline improvements or net-new solutions where current practices fall short.
Monitor pipeline health, troubleshoot data quality issues, and proactively resolve performance and reliability problems.
Manage and evolve orchestration tooling with openness to adopting better alternatives as infrastructure needs grow.
Optimize query performance, pipeline efficiency, and resource utilization across Convo’s data environment.
Participate in testing, deployment, and monitoring practices that promote long-term reliability and scalability.
Develop and maintain scalable data transformation processes, schema design, and data models that support evolving business requirements.
Establish and evolve data quality testing frameworks - building practices that catch issues early and create lasting internal trust in our data.
Own data governance, documentation, lineage, version control, and data quality standards across the organization.
Serve as the primary internal resource for data engineering guidance and recommendations, helping set standards and informing data infrastructure decisions across the organization.
Work closely with the data analyst to translate business questions into reliable, queryable data structures.
Educate and guide non-technical stakeholders on how to work effectively with data, what is and isn’t feasible, and how to frame data requests clearly.
Explore and implement tooling to enable self-service data discovery for internal teams, reducing bottlenecks and empowering stakeholders to answer their own questions.
Collaborate with Product, Engineering, Finance, Operations, and Data Science stakeholders to support reporting, forecasting, and business intelligence needs.
Partner 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 — building a foundation that is sustainable, well-understood, and not dependent on any single person.
Contribute to improvements in data architecture, tooling, monitoring, automation, and engineering best practices.
Evaluate emerging technologies and tooling to improve efficiency, automation, and accessibility of data systems.
Maintain clear technical documentation and operational standards that support long-term maintainability.
Exercise sound technical judgment in balancing immediate business needs with long-term platform sustainability.
Maintain strong confidentiality and discretion when handling sensitive organizational, financial, operational, and employee data.

Requirements

What you’ll need

Strong SQL skills with hands-on 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 version control best practices.
Solid understanding of ETL/ELT patterns, pipeline orchestration, and modern data modeling concepts.
Experience managing and supporting production-grade data infrastructure and pipelines.
Demonstrated ability to work independently, self-direct priorities, and make sound technical decisions without day-to-day oversight.
Experience troubleshooting data quality, reliability, and performance issues within complex data environments.
Ability to communicate technical concepts clearly and guide non-technical stakeholders on data capabilities and limitations.
A collaborative mindset and comfort working across teams with varying technical backgrounds.
Openness to inheriting existing systems and the judgment to know when to improve versus rebuild.
Openness to learning new tools and technologies as the data engineering landscape continues to evolve.
Ability to handle sensitive and confidential information with strong integrity and professionalism.

Benefits

Comp & perks

Health insurance
401(k) matching
Flexible work hours
Paid time off

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

SQLSnowflakePythondbtETLELTdata modelingpipeline orchestrationdata transformationdata quality

Soft Skills

self-directiontechnical judgmentcommunicationcollaborationproblem-solvingintegrityprofessionalismguidanceprioritizationadaptability