Data Engineer (Data Product)

Location

New York City, NY

On-site role requiring five days per week in the Williamsburg, Brooklyn office. Relocation support is available.

Compensation

$180,000 – $225,000 Base + Competitive Equity

Flexibility above $225,000 for exceptional candidates.

Visa

Open to visa transfers, including H-1B and TN.

Company Stage

High-Growth / Venture-Backed Technology Company

Industry

Artificial Intelligence, Data Infrastructure, Marketing Technology, Consumer Data, Data Products, SaaS, B2B, Enterprise Technology

About the Company

Our client is building AI-powered technology for marketing and revenue teams, using a proprietary consumer data graph to power intelligent sales and marketing automation.

The company works with massive-scale consumer datasets spanning hundreds of millions of U.S. consumers and thousands of attributes, transforming complex data into products that help enterprise customers make better decisions.

The data organization sits at the intersection of data engineering, applied data science, and product development.

This is not a traditional infrastructure-focused Data Engineering role. Instead, engineers are responsible for deeply understanding the underlying data, modeling messy real-world datasets, building production pipelines, and turning raw signals into trusted, monetizable data products.

The work is highly customer-facing in its impact. Data products built by the team can become directly sellable to enterprise customers and contribute to revenue shortly after launch.

The ideal candidate is intellectually curious, highly proficient in Python and SQL, comfortable investigating unfamiliar datasets, and capable of independently determining what data should be built and how it should be modeled.

This is an opportunity to join a high-growth team where data engineers have direct ownership over data domains and where the output of engineering work directly influences customer products and revenue.

What You'll Do

• Own complex consumer-data domains end-to-end
• Investigate large, messy, and unfamiliar datasets
• Determine dataset grain, relationships, coverage, quality, and suitability for product use cases
• Design durable domain models and derived attributes
• Build production-grade data pipelines using Python and SQL
• Build ingestion and transformation pipelines that bring data products into production
• Develop data validation and quality frameworks
• Build observability into production data pipelines
• Manage data backfills and historical data processing
• Investigate new external data sources and evaluate their potential value
• Conduct "data quests" to identify new sources of consumer and business intelligence
• Determine how new datasets can improve existing data products
• Extract meaningful signals from imperfect or incomplete datasets
• Become an internal expert in one or more complex data domains
• Develop deep understanding of consumer data and how it can be productized
• Work across domains such as identity graphs, property data, consumer intent, and related datasets
• Determine what data is useful, what may be misleading, and what should be built next
• Partner with Data Scientists to productionize data-driven insights
• Partner with Data Platform Engineers on infrastructure and pipeline requirements
• Collaborate with customer-facing teams to understand enterprise data needs
• Translate ambiguous business questions into scalable data products
• Build data products that directly support customer-facing applications
• Develop datasets and attributes that can be monetized by go-to-market teams
• Work closely with Product and business stakeholders to prioritize high-value data opportunities
• Design and maintain scalable transformation workflows
• Improve data quality, reliability, and accessibility
• Build reusable data models and production datasets
• Analyze large-scale data to identify patterns, gaps, and opportunities
• Solve complex data problems using first-principles reasoning
• Work independently to determine the appropriate technical approach
• Balance speed, data quality, and product requirements
• Take ownership of data products from initial investigation through production
• Continuously improve existing pipelines and data products
• Help establish best practices for data modeling, testing, and productionization
• Contribute to the evolution of the company's consumer data platform
• Work closely with engineering and data leadership on technical strategy
• Build data systems that directly support revenue-generating products

Ideal Candidate Background

Experience Requirements

• 2+ years of experience in Data Engineering, Applied Data Science, Analytics Engineering, or related roles
• Open to mid-level and senior candidates
• Strong candidates with approximately 2–5 years of experience are particularly relevant
• Exceptional junior candidates may be considered if they demonstrate unusually strong technical ability and learning velocity
• Strong production experience working with Python and SQL
• Experience building data pipelines that power customer-facing products
• Experience working with large, messy, multi-source datasets
• Experience modeling complex real-world data
• Experience cleaning, transforming, and enriching raw data
• Experience turning raw datasets into trusted production outputs
• Experience building data products rather than only internal reporting systems
• Experience working closely with the underlying data rather than only infrastructure
• Experience solving ambiguous data problems independently
• Experience translating business requirements into production data systems
• Experience working on product or revenue-driving teams strongly preferred
• Experience at an early-stage or high-growth startup preferred
• Experience working with consumer data is a strong plus
• Experience working with large-scale datasets preferred
• Strong analytical and problem-solving ability
• Strong product mindset
• Strong intellectual curiosity
• Ability to understand what data means in the real world
• Ability to identify opportunities within unfamiliar datasets
• Comfortable operating independently in a fast-moving environment

Technical Requirements

• Highly proficient in Python
• Highly proficient in SQL
• Strong data engineering fundamentals
• Strong data modeling experience
• Experience designing production data pipelines
• Experience with ingestion and transformation workflows
• Experience working with large-scale datasets
• Experience handling messy and inconsistent data
• Strong data cleaning and normalization skills
• Experience building production-quality datasets
• Experience with data validation
• Experience with data quality monitoring
• Experience with pipeline observability
• Experience managing backfills
• Strong understanding of relational data
• Strong understanding of data schemas and domain modeling
• Ability to reason about data grain and relationships
• Ability to identify data quality issues
• Ability to design durable data models
• Experience with orchestration tools such as Dagster or Airflow
• Experience with transformation tools such as dbt
• Experience with distributed data processing such as Spark
• Experience with modern data warehouses such as Snowflake
• Experience with data lake technologies such as Apache Iceberg is a plus
• Experience with query engines such as Trino is a plus
• Experience working with APIs and external data sources is a plus
• Experience evaluating and integrating third-party datasets is a plus
• Experience building data products for customer-facing applications
• Strong understanding of data reliability and production operations
• Ability to write maintainable and production-quality code
• Ability to debug complex data pipelines
• Strong SQL query optimization and data investigation ability
• Ability to work across data engineering and applied data science problems

Education

• Bachelor's degree in Computer Science, Mathematics, Engineering, Data Science, or a related technical discipline preferred
• Strong academic background in quantitative or technical fields is a plus
• Top-tier CS, mathematics, or engineering programs are particularly relevant for candidates with 0–1 years of experience
• Strong professional experience can compensate for academic pedigree at more experienced levels

Soft Skills

• First-principles problem solver
• Strong intellectual horsepower
• Highly analytical
• Naturally curious about data
• Strong product thinking
• Customer-oriented mindset
• Revenue-oriented mindset
• Comfortable working with ambiguity
• Able to independently determine what should be built
• Strong business judgment
• Strong communication skills
• Strong cross-functional collaboration
• Comfortable working with Data Scientists
• Comfortable working with Platform Engineers
• Comfortable working with customer-facing teams
• Comfortable translating business questions into technical solutions
• Strong ownership mentality
• High agency
• Bias toward action
• Comfortable operating without detailed instructions
• Strong attention to detail
• Comfortable investigating unfamiliar datasets
• Willing to challenge assumptions
• Creative approach to finding useful data signals
• Strong problem-solving skills
• Able to balance technical rigor with business needs
• Comfortable working in a fast-paced startup environment
• Strong learning ability
• Able to quickly develop expertise in unfamiliar data domains
• Comfortable working directly with data rather than staying abstracted from it
• Motivated by building things that customers actually use
• Comfortable working five days per week in New York City

Compensation & Benefits

• Base Salary: $180,000 – $225,000
• Flexibility above $225,000 for exceptional candidates
• Competitive equity package
• Opportunity to build customer-facing data products
• Direct impact on revenue-generating products
• Opportunity to work with large-scale consumer datasets
• Exposure to cutting-edge AI and data infrastructure
• High ownership over data domains and products
• Opportunity to work closely with Data Science and Engineering teams
• Opportunity to influence how data products are built and monetized
• High-growth technology environment
• Opportunity to solve complex data problems at significant scale
• Opportunity to develop deep expertise in consumer data
• Relocation support available
• Visa transfer support, including H-1B and TN
• High degree of autonomy and technical ownership

Why Join

This is an opportunity to join a high-growth AI and data company where data engineering is directly connected to product development and revenue.

Unlike traditional Data Engineering roles focused primarily on infrastructure, streaming, or internal reporting, you'll work deeply with the data itself — understanding messy real-world datasets, modeling them, extracting useful signals, and turning them into production-ready products.

Your work can become a sellable customer-facing data product within weeks, meaning you'll see a direct connection between the systems you build and the company's commercial success.

You'll work with massive consumer datasets and have the opportunity to become an expert in complex data domains such as identity, property, consumer intent, and other high-value datasets.

The role is ideal for someone who enjoys going deep into unfamiliar data, solving ambiguous problems from first principles, and independently figuring out what should be built.

If you want to combine data engineering, applied data science, product development, and business impact, this role offers significant ownership and exposure.