Builds and maintains reliable data pipelines and infrastructure using SQL, Python, Airflow, and Redshift to support analytics for Risk, Marketing, Finance stakeholders. Leads projects, reviews code, and scales platform as business grows.
Salary not listed
On-siteData Engineering
About the role
Responsibilities
First Week:
Introduce yourself on Slack and meet your team.
Get oriented in the warehouse, walk through most-used pipelines, read codebase to ask useful questions.
Ship your first commit to production (bug fix, documentation update, or small improvement).
First Month:
Get fluent in Airflow setup, Redshift conventions, and DataHub governance tooling.
Complete first end-to-end pipeline or pipeline change in collaboration with a downstream team (Risk, Marketing, Finance, etc.).
Start reviewing PRs from other data engineers, providing pragmatic feedback.
First Three Months:
Be technical lead on a meaningful project from design through implementation, testing, and rollout.
Participate in architecture discussions and contribute to platform direction.
Build stakeholder trust so they come to you directly for data issues.
First Year:
Own a slice of infrastructure roadmap as product scales.
Become go-to expert on one or more platform areas.
Help hire and onboard new data engineers.
Enable teammates to ship projects independently on your services.
Requirements
Experience with healthy data engineering culture, data quality, pipeline reliability, stakeholder communication.
Led data pipeline or platform design decisions, including trade-offs.
Comfort across SQL, Python, orchestration, and infrastructure-as-code.
Favorite SQL patterns, modeling approaches, or data craft.
What You'll Learn
Perpay's payroll-deduction business model and data implications.
Building reliable data products: scoping, modeling, code review, testing, observability, lineage.
Data team roadmap and business alignment.
Stakeholders across Risk, Commerce, Marketing, Ops, Finance.
Compensation & Benefits
Meaningful compensation and equity.
Premium medical benefits (fully paid base plan).
4% employer 401k match.
Unlimited PTO.
Remote weeks around major holidays + extra holidays.
High quality catered lunch 4 days/week.
Gym subsidy.
Paid cell phone + plan.
Student loan repayment.
Relocation assistance.
Generous team member discounts.
Skills
SQLPythonAirflowRedshiftDatahubData PipelinesInfrastructure As CodeData ModelingObservabilityData Lineage
Senior Analytics Engineer owning OnePay's dbt models, Databricks BI, data quality, and semantic layers on a fast-moving fintech team. Requires 5+ years production analytics engineering, expert SQL/dbt, Databricks experience, and daily AI coding tool usage.
130k – 170k/yr
Remote5+ YOEData Engineering
Forward Deployed Data Engineer (Integration)
HilbertSan Francisco, CA
Forward Deployed Data Engineer building hybrid data pipelines and semantic layers for Hilbert's AI Growth Engine. Implements warehouse-native or managed ClickHouse integrations, partners with AI agents for accelerated onboarding, and ensures reasoning consistency across customer environments.
Salary not listed
HybridData Engineering
Software Engineer, Data Infrastructure
The Voleon GroupNew York, NY +1
Software Engineer building scalable data infrastructure, cataloging, versioning, and lineage tools to support ML research and production workflows at an AI-driven hedge fund. Requires 3+ years experience, strong software design skills, and expertise in a modern language like Python or Java.
235k – 300k/yr
Remote3+ YOEData Engineering
Client Delivery Specialist
Hinge HealthSan Francisco, CA
Manage end-to-end file-based data integrations, ingestion, transformation, and maintenance for eligibility, marketing, and reporting. Own data integrity, resolve issues, automate workflows with AI, and partner cross-functionally with Customer Success, Engineering, and Revenue Operations teams.
80k – 120k/yr
HybridData Engineering
Software Engineer
xAIPalo Alto, CA
Build and operate realtime and batch data pipelines processing billions of events daily at xAI. Design distributed data platforms, own data correctness, create shared datasets for product and business teams, and partner on data acquisition using tools like Spark, Kafka, Flink, and SQL.