Moving audiences to action through connected experiences.
Data Engineer
Location
United States
Posted
9 hours ago
Salary
$100K - $110K / year
Seniority
Senior
Job Description
Data Engineer
InVision Communications
• 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.
Job 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.
Benefits
- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
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