Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
An AI-native automation company is transforming how enterprises operate through intelligent agents that understand real-world workflows and execute them end-to-end. Its technology replaces manual processes and rigid legacy systems with adaptive, learning software.
Key Responsibilities
- Build and own the organization’s first unified data warehouse, including data modeling, pipelines, data quality, and performance.
- Partner with teams across the business to translate ambiguous questions into clear requirements and standardized business metrics.
- Consolidate data from production PostgreSQL databases and SaaS systems, including product usage, CRM, marketing, billing, support, and finance systems.
- Establish a single source of truth and deliver clean, reliable data back into the tools used by business teams.
- Design clean and performant data models with clear lineage, standardized terminology, and comprehensive documentation.
- Develop internal data tools using applied AI and intelligent agents to provide self-service answers to recurring data questions.
- Establish data quality, governance, and security practices from the beginning.
- Make vendor, tooling, and architecture decisions for the data stack while maintaining a lean and cost-conscious approach.
- Define data engineering practices, standards, and processes that can support future team growth.
- Collaborate closely with Engineering, Product, go-to-market, and operations teams to understand business needs and deliver actionable data solutions.
Required Qualifications
- 5+ years of experience in data engineering and business analytics, including hands-on experience building and maintaining data warehouses and pipelines.
- Expert-level SQL skills, including hands-on experience with PostgreSQL.
- Strong Python skills for pipeline development, scripting, and data tooling.
- Experience building a modern data warehouse or lakehouse from scratch using technologies such as Snowflake, Databricks, or comparable platforms.
- Experience building ETL/ELT pipelines using modern data-stack technologies such as Fivetran and dbt.
- Hands-on experience with AWS and infrastructure as code using Terraform for cloud data infrastructure.
- Demonstrated ability to own data projects end to end and make sound independent technical decisions.
- Experience working as an early or first data hire is highly desirable.
- Strong product intuition and business curiosity, with the ability to translate business questions into practical and reliable data models.
- Ability to determine meaningful business metrics and collaborate effectively with both technical and non-technical stakeholders.
- Comfortable working in a fast-growing startup environment characterized by rapid change and ambiguity.
Nice to Have
- Experience building internal data tools or AI agents using LLMs.
- Familiarity with modern data-stack technologies for orchestration, transformation, and streaming.
- Familiarity with technologies such as Go, gRPC, React, TypeScript, Kubernetes, AWS, and Terraform.
- Experience establishing self-service analytics platforms and capabilities.
- Early-stage startup experience or a strong track record of zero-to-one development.
- Bachelor’s degree in Computer Science or a related engineering discipline.
Disclaimer
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- No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
- No Client Relationship: This company is not a client of SpringCube unless stated.
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