5+ years of experience in data engineering, analytics engineering, or a highly technical data role.
Proficient in building and maintaining ETL/ELT pipelines using modern tools.
Familiarity with data warehousing, transformation frameworks, BI tools, and event analytics systems.
Strong grasp of data quality principles, testing, documentation, and analytics reliability.
Proficient in SQL and capable of building production-grade data models.
Ability to convert complex business questions into actionable data models and dashboards.
Experience collaborating with engineering teams on data infrastructure and tracking requirements.
Cross-functional experience with product, finance, marketing, and leadership teams.
Responsibilities
Own Venn's data infrastructure for key business decisions.
Build reliable ETL and ELT pipelines from various data sources.
Design accessible and understandable data models for company-wide use.
Collaborate with engineering on data tracking and measurement architecture.
Enhance data quality, observability, and governance across the stack.
Empower non-technical teams to leverage data tools for analysis.
Create dashboards and reports for essential company metrics.
Support marketing and sales measurement initiatives and provide actionable insights.
Benefits
Competitive salary.
Latest tech and equipment.
Full Job Description
What You'll Be Doing
Own Venn's data infrastructure, including pipelines, models, warehouses, and reporting layers that power product, finance, risk, operations, marketing, and leadership decisions.
Build reliable ETL and ELT pipelines that ingest, transform, and structure data from our core product, banking partners, card processors, payment systems, accounting integrations, CRM, marketing tools, and internal systems.
Design clean, trusted data models and analytics layers that make business-critical data easy to access, understand, and use across the company.
Partner with engineering on event schemas, tracking requirements, and measurement architecture to ensure data is reliable, scalable, and built correctly from the start.
Improve data quality, observability, documentation, lineage, testing, and governance across the stack.
Empower teams across Venn to use AI-assisted workflows, MCPs, and trusted data tools to safely query data, run analysis, and answer business questions independently.
Build dashboards, reports, and self-serve workflows for core company metrics, including transaction volume, card spend, FX, balances, revenue, activation, retention, onboarding conversion, acquisition, and risk signals.
Support marketing, growth and sales measurement where needed, including attribution, paid media incrementally, web analytics, CRO, lifecycle analytics, SEO, and organic acquisition reporting.
Help translate complex data into clear recommendations that influence product, GTM, finance, and leadership decisions.
What You'll Need
5+ years of experience in data engineering, analytics engineering, or a highly technical data role.
Experience building and maintaining reliable ETL or ELT pipelines using modern data tooling.
Experience working with data warehouses, transformation frameworks, BI tools, and event or product analytics systems.
Strong understanding of data quality, testing, documentation, lineage, metric definition, and analytics reliability.
Strong SQL skills and experience building production-grade data models.
Ability to translate ambiguous business questions into clean data models, dashboards, and clear recommendations.
Experience partnering with engineering teams on tracking requirements, event schemas, and data infrastructure.
Comfort working cross-functionally with product, engineering, finance, operations, growth, marketing, RevOps, and leadership teams.
Experience with marketing, growth, or web analytics, including attribution, funnel analysis, paid media measurement, SEO analytics, lifecycle analytics, or CRO.
Strong communication skills, with the ability to explain technical data concepts and analytical findings in a way that drives business decisions.
Bonus Points
Experience at an early-stage or fast-growing startup.
