ZipRecruiter
Sr. Data Engineering Manager (Remote)
ZipRecruiter, San Francisco, California, United States, 94199
Overview
Senior
Engineering Manager
— We’re looking for a strong data leader to build and scale our data function in a high-growth environment. You will lead data engineering, analytics engineering, and automation initiatives; owning everything from high-scale ingestion pipelines to AI agents that streamline workflows. This role is ideal for someone who thrives in early-stage (Series A–C) fintech or hedge fund environments and wants to have direct impact on business outcomes.
Responsibilities
Data Platform & Pipelines
Own all ETL/ELT from external marketplaces and partners (listings, sold history, and transaction data).
Deliver broad coverage, freshness, and low-latency pipelines across APIs, feeds, and compliant web scraping.
Contribute to AI-driven detection, entity resolution, and robust outlier cleansing.
Set measurable SLOs for freshness, latency, and accuracy; implement monitoring, alerting, and runbooks.
Manage and expand the internal asset catalog to grow proprietary datasets.
Analytics Engineering
Lead the analytics engineering function, partnering closely with analysts.
Build a self-service metrics layer and executive dashboards covering GMV, liquidity, funnels, buyer/seller behavior, and ops SLAs.
Own the event taxonomy and product analytics in Amplitude.
Design and manage A/B experimentation frameworks.
Automation & AI Agents
Identify high-leverage manual workflows (QA, price suggestions, dispute resolution, KYC, payout reconciliation).
Deliver AI agents and automation systems to reduce manual work and increase efficiency.
Partner with Product, Ops, Risk, and Finance to prioritize automation by ROI and impact.
Leadership & Strategy
Lead and grow a multidisciplinary team spanning data engineering, analytics engineering, and automation/ML engineering.
Manage vendor relationships and budgets.
Translate business objectives into a clear roadmap with measurable quarterly outcomes.
Tech Stack You’ll Work With
Data Warehouse/Lake:
Snowflake, S3
Orchestration & Streaming:
Airflow, data streaming services
Ingestion:
Airbyte, Fivetran, Scrapy, Playwright
Transformations & Quality:
dbt, Great Expectations, Soda, data catalog
Search & Analytics:
Typesense, Tableau, Metabase, Amplitude
Infra/DevOps:
AWS, Terraform/IaC, GitHub Actions, Datadog
AI/Agents:
OpenAI, Anthropic, Vertex AI, Langchain, Pydantic AI, embeddings, RAGs, evaluation/guardrails
What You Bring
8–12+ years in data
engineering/analytics , with at least 4+ years in hands-on leadership roles (managing managers and cross-functional teams).
Proven track record of building and operating large-scale data ingestion pipelines across diverse sources with strong SLAs.
Experience leading analytics engineering functions—partnering with analysts on metric definitions, experimentation, and executive reporting.
Strong proficiency in
Python
and
SQL , with the ability to perform code reviews and contribute to schema/model design.
Experience implementing LLMs/agent systems in production with measurable outcomes.
Background in fintech, hedge funds, or marketplaces (pricing, risk, payments strongly).
Startup experience in high-growth environments (Series A–C) with prior success scaling teams.
Compensation
$185-$230k base salary + equity
#J-18808-Ljbffr
Engineering Manager
— We’re looking for a strong data leader to build and scale our data function in a high-growth environment. You will lead data engineering, analytics engineering, and automation initiatives; owning everything from high-scale ingestion pipelines to AI agents that streamline workflows. This role is ideal for someone who thrives in early-stage (Series A–C) fintech or hedge fund environments and wants to have direct impact on business outcomes.
Responsibilities
Data Platform & Pipelines
Own all ETL/ELT from external marketplaces and partners (listings, sold history, and transaction data).
Deliver broad coverage, freshness, and low-latency pipelines across APIs, feeds, and compliant web scraping.
Contribute to AI-driven detection, entity resolution, and robust outlier cleansing.
Set measurable SLOs for freshness, latency, and accuracy; implement monitoring, alerting, and runbooks.
Manage and expand the internal asset catalog to grow proprietary datasets.
Analytics Engineering
Lead the analytics engineering function, partnering closely with analysts.
Build a self-service metrics layer and executive dashboards covering GMV, liquidity, funnels, buyer/seller behavior, and ops SLAs.
Own the event taxonomy and product analytics in Amplitude.
Design and manage A/B experimentation frameworks.
Automation & AI Agents
Identify high-leverage manual workflows (QA, price suggestions, dispute resolution, KYC, payout reconciliation).
Deliver AI agents and automation systems to reduce manual work and increase efficiency.
Partner with Product, Ops, Risk, and Finance to prioritize automation by ROI and impact.
Leadership & Strategy
Lead and grow a multidisciplinary team spanning data engineering, analytics engineering, and automation/ML engineering.
Manage vendor relationships and budgets.
Translate business objectives into a clear roadmap with measurable quarterly outcomes.
Tech Stack You’ll Work With
Data Warehouse/Lake:
Snowflake, S3
Orchestration & Streaming:
Airflow, data streaming services
Ingestion:
Airbyte, Fivetran, Scrapy, Playwright
Transformations & Quality:
dbt, Great Expectations, Soda, data catalog
Search & Analytics:
Typesense, Tableau, Metabase, Amplitude
Infra/DevOps:
AWS, Terraform/IaC, GitHub Actions, Datadog
AI/Agents:
OpenAI, Anthropic, Vertex AI, Langchain, Pydantic AI, embeddings, RAGs, evaluation/guardrails
What You Bring
8–12+ years in data
engineering/analytics , with at least 4+ years in hands-on leadership roles (managing managers and cross-functional teams).
Proven track record of building and operating large-scale data ingestion pipelines across diverse sources with strong SLAs.
Experience leading analytics engineering functions—partnering with analysts on metric definitions, experimentation, and executive reporting.
Strong proficiency in
Python
and
SQL , with the ability to perform code reviews and contribute to schema/model design.
Experience implementing LLMs/agent systems in production with measurable outcomes.
Background in fintech, hedge funds, or marketplaces (pricing, risk, payments strongly).
Startup experience in high-growth environments (Series A–C) with prior success scaling teams.
Compensation
$185-$230k base salary + equity
#J-18808-Ljbffr