Data Engineer

Overview

We're looking for a Data Analytics Engineer to join our data team and help build and scale our analytics infrastructure from ingestion to dashboard. You'll build and maintain the pipelines, transformations, and reporting layers that the rest of the company relies on to understand our product, our customers, and our business. This role sits at the intersection of data engineering and BI analytics, and the right person will be equally comfortable writing complex SQL transformations and presenting a clean dashboard to stakeholders.

You’ll work closely with product engineering to instrument new features, maintain the business logic behind our core metrics, and keep our data warehouse performant and cost‑efficient. There’s also real opportunity to push into real‑time data infrastructure as the platform evolves, including streaming pipelines and event‑driven metric delivery for production services.

• Build and maintain analytics pipelines in Big Query using dbt Cloud, including staging, transformation, and mart layers
• Manage and configure Airbyte ingestion streams and Hightouch reverse ETL syncs
• Partner with product engineering to ensure new features follow our event‑driven instrumentation methodology and emit the right data from day one
• Define and maintain business logic for metrics construction, serving as the source of truth for how key metrics are calculated
• Build, update, and maintain internal dashboards in Looker Studio
• Build and maintain Big Query datasets, views, and scheduled queries
• Monitor and optimize query performance and warehouse costs, proactively identifying opportunities to reduce spend
• Design and implement data backup and redundancy strategies
• Make occasional updates to the customer‑facing dashboard layer in the product (Type Script)
• Perform ad‑hoc data analysis and produce one‑off data pulls for customers as needed
• Identify recurring data requests and build automated, self‑serve solutions to replace manual fulfillment
• Investigate Big Query continuous queries for publishing metrics to Cloud Pub/Sub, enabling product engineering services to consume analytics data in near real‑time
• Evaluate Cloud Dataflow for real‑time and low‑latency data processing use cases as the platform's needs evolve

• 5+ years of experience in data engineering, BI analytics, or software engineering
• Expert‑level SQL skills, particularly in Big Query or comparable cloud data warehouses
• Hands‑on experience with dbt (dbt Cloud preferred) for managing transformation pipelines
• Experience with data ingestion tools such as Airbyte, Fivetran, or similar
• Experience with reverse ETL tools such as Hightouch or Census
• Working knowledge of Python for data scripting, automation, and ad‑hoc analysis
• Experience building and maintaining dashboards in Looker Studio or similar BI tools
• Strong understanding of event‑driven data instrumentation and how analytics contracts should be defined alongside product features
• Track record of optimizing data warehouse costs and query performance
• Comfort working directly with engineering teams to define data requirements and instrumentation standards

• Experience with GCP data services beyond Big Query (Cloud Dataflow, Pub/Sub, Cloud Functions)
• Familiarity with Type Script or Kotlin
• Experience building or contributing to customer‑facing analytics features within a product
• Prior work with streaming or real‑time data architectures
• Experience implementing data quality testing frameworks (e.g., dbt tests, Great Expectations, or similar)
• Familiarity with data governance practices, including documentation, lineage tracking, and access controls
• Experience working at a fast‑growing startup where you owned the data stack end to end

New York City

Compensation for this role is location‑based and benchmarked against local market data aligned to the employee’s primary work location. Total compensation includes a mix of cash and equity and may vary by location, role level, and experience. Our range is therefore wide and not meant as a negotiation range.