Sr Data Engineer

Senior Data Engineer

Data & Analytics

About the Role

We are hiring a Senior Data Engineer to shape and strengthen the data foundation that drives company performance.

This is a senior individual contributor role with meaningful influence. You will define engineering standards, evolve our data platform, and ensure data is reliably integrated and structured to support confident decision making across the organization.

Success in this role requires more than technical execution. You will be expected to operate independently, bring structure to ambiguity, challenge unclear assumptions, and translate complex system and business problems into durable data solutions.

Key Responsibilities

Data Platform Leadership

Lead the development and continuous improvement of data pipelines and integration frameworks

Define standards for ingestion, orchestration, and data movement across the platform

Improve reliability, performance, and cost efficiency of data pipelines and storage

Design scalable, reusable patterns that support consistent data flow across systems

Data Integration and Architecture

Design and implement integrations across internal and external systems using tools such as Fivetran and custom pipelines

Own how data enters and moves through the warehouse, ensuring consistency and integrity

Structure data layers to support both upstream flexibility and downstream consumption

Ensure strong alignment between source system logic and warehouse representations

Data Modeling and Foundation

Architect and implement core data models in Snowflake using dbt

Build durable data layers aligned to core revenue and operational domains

Ensure strong alignment between upstream data structures and downstream reporting needs

Embed testing, documentation, and performance optimization into standard engineering practice

Data Reliability and Quality

Establish and enforce data quality standards across ingestion and transformation layers

Design systems for validation, monitoring, and lineage

Proactively identify and resolve data issues, reducing ambiguity and downstream impact

Strengthen trust in enterprise data through consistent and reliable delivery

AI-Enabled Data Systems

Treat AI as a core capability in modern data engineering, leveraging it to deliver high-quality solutions with greater speed and rigor

Design data pipelines and structures that enable safe and effective use of AI with trusted data

Integrate AI into engineering workflows to accelerate development, monitoring, and optimization

Apply disciplined judgment when evaluating AI-generated outputs, ensuring enterprise standards for accuracy and consistency

Establish practical guardrails for responsible AI usage across both engineering and business consumption

Cross-Functional Partnership

Translate complex and ambiguous business questions into scalable data solutions

Communicate technical tradeoffs clearly to both technical and business stakeholders

Introduce platform and process improvements and drive them through implementation

Influence data standards across the Data & Analytics function

What We Look For

Ability to operate independently in complex, evolving environments

Clear and structured communication

Strong analytical reasoning and systems thinking

Ownership mindset with focus on measurable business outcomes

Willingness to challenge assumptions constructively

High standards for data quality, reliability, and engineering rigor

Qualifications

6+ years of experience in data engineering or analytics engineering

Strong experience with data integration, pipeline development, and warehouse architecture

Strong hands-on experience with Snowflake and dbt

Experience with data integration tools such as Fivetran

Experience working with cloud platforms such as AWS

Advanced SQL with demonstrated performance tuning expertise

Experience with Git-based workflows and GitHub for version control

Experience building and operating reliable data pipelines

Preferred:

Experience in SaaS or subscription-based businesses

Experience working with global stakeholders

Python for automation or platform tooling

Impact

The systems and standards established in this role will directly influence data reliability, operational efficiency, and decision making. The quality of this foundation will determine how effectively the organization leverages both trusted data and AI to drive performance.

How We Think About AI

At Quickbase, we view AI as a tool to accelerate how work gets done not replace it. We encourage thoughtful use of AI to improve speed, quality, and decision-making, while maintaining strong judgment, accountability, and data integrity.