Senior Data Engineer

zipcolimited; United States; Remote; USD 170,000 - 210,000; Full time

Benefits offered by this job

Remote-first policy

US-based role

Manhattan office option

Job summary

Zip is hiring a Senior Data Engineer to build and evolve our data platform powering analytics across risk, finance, product, and operations. You will own scalable pipelines, trusted data models, and reusable data products on a modern cloud platform.

The ideal candidate brings 5+ years designing production data pipelines, advanced SQL and Python, and hands-on experience with Snowflake, Databricks, and dbt. You’ll partner with analytics, risk, and ML teams in a remote-first US-based role with an

Qualifications

• 5+ years designing, building, and operating scalable data pipelines in production environments.
• Strong SQL and Python, with production-quality code.
• Hands-on experience with Snowflake, Databricks, and cloud data platforms.
• Solid data modeling fundamentals and dimensional modeling.
• Experience with ETL/ELT tooling (dbt) and orchestration (Airflow).
• Experience with streaming, CDC, or event-driven architectures and data quality/observability.

Responsibilities

• Design, build, and operate scalable data pipelines across our cloud platform.
• Develop trusted, reusable data products and semantic models for analytics and business use.
• Build and optimize ETL/ELT workflows with modern frameworks (dbt, Airflow).
• Strengthen data quality, reliability, and observability with automated checks.
• Design near-real-time data capabilities for monitoring, risk, and product needs.
• Collaborate with Data Science and ML teams to support AI-ready datasets.

Skills

SQL

Python

Data modeling

Production ownership

Observability

Cloud data platforms

Tools

Snowflake

Databricks

dbt

Airflow

Kafka

Azure

Job description

Build and own scalable data pipelines, modern cloud data platforms, and trusted data products that power critical business decisions across Zip.

Apply strong SQL, Python, data modeling, and modern data engineering practices to solve complex problems at scale.

Influence data architecture, improve platform reliability, and help shape the future of AI-ready data capabilities.

Remote-first opportunity for US-based employees with the option to work in-person out of our Manhattan office.

Start your adventure with Zip

We are seeking a collaborative, hands-on Senior Data Engineer to help build and evolve the data platform powering analytics, risk, finance, product, and critical business decisions across Zip.

In this role, you'll design, build, and operate scalable data pipelines, trusted data models, and reusable data products across our modern cloud data platform. You'll take ownership of complex engineering challenges, improve data quality and reliability, and help ensure our data is accurate, accessible, governed, and ready to support both existing business needs and emerging AI-powered products.

The ideal candidate brings strong Data Engineering fundamentals, experience owning production systems, and the technical judgment to design scalable solutions while balancing performance, reliability, and cost.

Our Data Engineering team plays a critical role in enabling teams across Zip to make faster, more informed decisions. We work closely with Analytics, Risk, Product, Data Science, Machine Learning, and Engineering to solve complex problems, modernize our data capabilities, and build a trusted foundation for future innovation.

We value engineers who take ownership, remain hands-on, challenge existing approaches, and enjoy making the broader engineering team more effective.

Interesting problems you'll solve

• Design, build, and operate scalable batch, streaming, and event-driven data pipelines that integrate transactional systems, APIs, third-party providers, and enterprise data sources into our modern cloud data platform.
• Develop trusted, reusable data products and dimensional and semantic models that support analytics, risk, finance, product, and operational decision-making.
• Build and optimize ETL/ELT workflows using modern transformation and orchestration frameworks, improving data processing efficiency, scalability, and maintainability.
• Strengthen data quality, reliability, and observability through automated validation, reconciliation, freshness monitoring, anomaly detection, and proactive alerting.
• Design near-real-time data capabilities supporting transaction monitoring, fraud and risk signals, and operational intelligence.
• Improve platform performance and cost efficiency while establishing scalable architectural patterns across our warehouse and lakehouse environments.
• Build shared frameworks, self-service capabilities, and automated workflows that reduce repetitive engineering effort and accelerate delivery across teams.
• Implement strong engineering practices around CI/CD, automated testing, infrastructure as code, documentation, governance, and production ownership.
• Partner with Data Science and Machine Learning teams to develop reliable, AI-ready datasets and data pipelines that support emerging intelligent products and applications.
• Influence technical architecture, independently lead complex initiatives, mentor engineers, and help raise the engineering bar across Data Engineering.

What you'll bring to the team

• 5+ years of experience designing, building, and operating scalable data pipelines and enterprise data platforms in production environments.
• Advanced SQL and strong Python skills, with experience developing maintainable, tested, production-quality code.
• Hands-on experience with modern cloud data platforms such as Snowflake, Databricks, Microsoft Fabric, BigQuery, Redshift, or equivalent technologies.
• Strong data modeling fundamentals, including dimensional modeling, fact and dimension tables, slowly changing dimensions, and reusable analytical data models.
• Experience building ETL/ELT workflows using modern transformation frameworks such as dbt or equivalent technologies, along with orchestration tools such as Airflow, Dagster, or similar platforms.
• Experience with streaming, change data capture (CDC), or event-driven architectures using Kafka, Azure Event Hubs, Spark Structured Streaming, or comparable technologies.
• Strong understanding of modern data architecture patterns, including data warehouses, data lakes, lakehouses, and curated data products.
• Experience implementing data quality, automated testing, monitoring, observability, metadata, lineage, and governance practices.
• Hands-on experience with a major cloud provider, preferably Azure, although AWS and GCP experience are equally transferable.
• Strong software engineering fundamentals, including Git, code reviews, CI/CD, infrastructure automation, and production troubleshooting.
• Ability to independently make archi