Data Engineer

Convo Communications

3h•$100,000 - $110,000

About The Position

Convo Communications is seeking a technically skilled and self-driven Data Engineer to support and advance the data infrastructure that powers operational, product, financial, and strategic decision-making across the organization. This is an individual contributor role where you will serve as Convo’s dedicated data engineer and primary internal resource for data infrastructure expertise and recommendations. You will inherit existing pipelines, tools, and practices and be expected to evaluate them thoughtfully: preserve what works, improve what doesn’t, and replace what should be rebuilt. We are looking for someone who brings informed opinions, establishes sound engineering standards, and helps mature and evolve the company’s data capabilities with confidence and ownership. This role requires a high degree of independence, technical judgment, and collaboration. The Data Engineer will partner closely with the Head of Product Operations, a dedicated data analyst, the CTO, and cross-functional stakeholders to ensure Convo has trusted, accessible, and reliable data to support business growth and operational performance.

Requirements

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.
Ability to work independently with a high degree of ownership, accountability, and technical judgment.
Comfortable operating as the primary data engineering resource within a cross-functional environment.
Ability to work a flexible schedule when needed to support business and operational priorities.
Strong interpersonal and professional communication skills.
Commitment to continuous learning and adapting to evolving technologies and business needs.

Nice To Haves

3+ years of experience serving as a sole or lead data engineer, with primary responsibility for a company’s data infrastructure.
3+ years of experience with AWS data services such as Glue, RDS, or similar technologies.
3+ years of experience with orchestration tools such as Stitch, Airflow, Prefect, or similar platforms.
3+ years of experience with data quality frameworks and testing practices.
3+ years of experience with BI and reporting tools.
Familiarity with CI/CD, testing, and deployment best practices for data infrastructure.
Familiarity with AI-adjacent data tooling and modern data infrastructure practices.
Experience working in a scaling technology or operations-driven organization.
Knowledge of American Sign Language (ASL) and/or Deaf culture is a plus.

Responsibilities

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.