Data Engineer - Product

Van Kaizen; New York (NY); On-site; USD 110,000 - 165,000; Full time

Job summary

Van Kaizen in New York City is seeking an experienced Data Engineer to join our on-site Brooklyn team. You will own consumer-data domains and build production data pipelines that power marketing AI features and insights.

You will apply Python and SQL to clean, model and analyze data at scale, collaborating with product and engineering teams to deliver reliable data products.

Qualifications

• 2-5+ years working as a data engineer, software engineer or applied data scientist in a data-heavy context.
• Highly proficient in Python and SQL.
• Driven by first-principles thinking.
• Strong intuition for data cleaning, ingestion and data modeling.
• Comfortable building and deploying production data pipelines.

Responsibilities

• Own consumer-data domains end-to-end and drive product-grade data.
• Investigate large, messy datasets.
• Define grain, keys, relationships and data quality.
• Design durable domain models powering apps, APIs and models.
• Identify new data sources and extract signal for ROI.
• Leverage LLMs and AI tools to improve data standardization and velocity.
• Create and protect business value from proprietary data assets.

Skills

Python

SQL

Data engineering

Data modeling

Production data pipelines

Job description

Rapidly growing AI platform for marketing leaders automating operational marketing work, is looking for Data Engineers with consumer facing product experience to join their New York City team!

To note: this is an on-site role to their office location in Brooklyn!

Responsibilities:

• Own one or more complex consumer-data domains end-to-end, becoming the person responsible for both understanding the data and advancing the products built from it.
• Investigate large, messy and unfamiliar datasets.
• Establish their grain, keys, relationships, coverage, failure modes and fitness for different product use cases.
• Design and build durable domain models, derived attributes and entity relationships that can power the company's applications, AI agents, APIs, customer deliveries and predictive models.
• Go on data quests: identify and evaluate new sources, determine how they can improve our consumer graph and find clever ways to extract signal from imperfect inputs. We often have budget for purchasing new data when there is clear ROI.
• Use LLMs, embeddings and modern AI development tools where they materially improve data standardization, classification, enrichment or engineering velocity.
• Find new ways to create and protect business value through the company's proprietary data asset, from improving existing products to opening entirely new revenue opportunities.

Qualifications:

• 2-5+ years working as a data engineer, software engineer or applied data scientist in a data-heavy context.
• Highly proficient in Python and SQL.
• Driven by first-principles thinking. You can take an ambiguous data problem, determine what must be true, interrogate the available evidence and design a practical path to an answer.
• Strong intuition for data cleaning, ingestion and data modeling. We expect these foundations to be second nature so your thinking is free for larger and more ambiguous data initiatives, especially given the leverage of modern AI coding tools.
• Comfortable building and deploying production data pipelines, not just analyzing data in notebooks or handing specifications to another engineering team.

Next Steps: