Data Engineer

RecruiterForge; United States; A distancia; USD 134.000 - 175.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Unlimited PTO

Parental leave

401(k) matching

Wellness stipend

Employer-sponsored insurance (employee

Union Square office optional

Descripción de la vacante

RecruiterForge is seeking a Senior Data Engineer to design, build, and operate cloud data pipelines and reusable data products for analytics and risk across the enterprise. You will combine SQL, Python, dimensional modeling, and production ownership to improve platform reliability and govern data for AI-powered products.

You will design scalable batch, streaming, and event-driven pipelines, develop reusable data models, and build ETL/ELT workflows using modern frameworks.

Formación

• Five+ years designing, building, and operating scalable data pipelines in production.
• Advanced SQL and strong Python for maintainable, production-grade code.
• Hands-on experience with cloud data platforms (Azure preferred; AWS/GCP transferable).
• Strong modeling fundamentals: fact/dimension tables, SCDs, reusable analytical models.
• Experience with ETL/ELT frameworks (dbt) and orchestration (Airflow/Dagster).
• Experience with streaming/CDC/event-driven architectures (Kafka, Event Hubs, Spark).
• Understanding of warehouses, lakes, lakehouses, governance, metadata, lineage, observability.
• Hands-on experience with a major cloud provider (Azure/AWS/GCP).
• Strong software engineering practices: Git, code reviews, CI/CD, IaC, production troubleshooting.
• Ability to make architectural decisions and improve scalability, performance, and cost.
• Experience translating requirements across Engineering, Analytics, Product, Risk, Finance, Data Science.

Responsabilidades

• Design and operate scalable batch, streaming, and event-driven pipelines across enterprise sources.
• Develop reusable data products and dimensional/semantic models for analytics and decision-making.
• Build and optimize ETL/ELT workflows with modern transformation/orchestration frameworks.
• Strengthen data quality and observability with automated validation and alerts.
• Create near-real-time capabilities for monitoring, risk signals, and operational intelligence.
• Improve performance and cost efficiency by establishing scalable architecture patterns.
• Build shared frameworks and automated workflows to accelerate delivery.
• Apply CI/CD, infrastructure as code, and production ownership practices.
• Partner with Data Science teams on reliable AI-ready datasets and pipelines.
• Lead complex initiatives, mentor engineers, and raise engineering standards.

Conocimientos

SQL

Python

Data modeling

ETL/ELT

CI/CD

Mentoring

Data quality

Observability

Cloud platforms

Azure

AWS

GCP

Airflow

dbt

Kafka

Herramientas

Snowflake

Databricks

BigQuery

Azure

AWS

GCP

Dagster

Airflow

Descripción del empleo

The Senior Data Engineer builds and operates cloud data pipelines, trusted data models, and reusable data products for analytics, risk, finance, product, and operational decisions. This hands-on role combines SQL, Python, dimensional modeling, and production ownership to improve platform reliability, govern data, and prepare datasets for emerging AI-powered products.

• Design and operate scalable batch, streaming, and event-driven pipelines that integrate transactional systems, APIs, third-party providers, and enterprise sources.
• Develop reusable data products and dimensional and semantic models for business and analytical decision-making.
• Build and optimize ETL/ELT workflows with modern transformation and orchestration frameworks, improving efficiency, scalability, and maintainability.
• Strengthen data quality and observability through automated validation, reconciliation, freshness monitoring, anomaly detection, and proactive alerts.
• Create near-real-time capabilities for transaction monitoring, fraud and risk signals, and operational intelligence.
• Improve performance and cost efficiency while establishing scalable architecture patterns across warehouse and lakehouse environments.
• Build shared frameworks, self-service capabilities, and automated workflows that reduce repetitive engineering work and accelerate delivery.
• Apply CI/CD, automated testing, infrastructure as code, documentation, governance, and production ownership practices.
• Partner with Data Science and Machine Learning teams on reliable, AI-ready datasets and pipelines for intelligent products and applications.
• Lead complex initiatives independently, influence architecture, mentor engineers, and establish stronger engineering standards.

Production Experience and Technical Judgment

• At least five years designing, building, and operating scalable data pipelines and enterprise data platforms in production.
• Advanced SQL and strong Python skills for maintainable, tested, production-quality code.
• Hands-on experience with cloud data platforms such as Snowflake, Databricks, Microsoft Fabric, BigQuery, Redshift, or equivalent technologies.
• Strong modeling fundamentals, including fact and dimension tables, slowly changing dimensions, and reusable analytical models.
• Experience with ETL/ELT transformation frameworks such as dbt and orchestration platforms such as Airflow, Dagster, or equivalents.
• Experience with streaming, change data capture, or event-driven architectures using Kafka, Azure Event Hubs, Spark Structured Streaming, or comparable tools.
• Understanding of warehouses, lakes, lakehouses, and curated data products, along with experience in testing, monitoring, observability, metadata, lineage, and governance.
• Hands-on experience with a major cloud provider. Azure is preferred; AWS and GCP experience are equally transferable.
• Strong software engineering practices across Git, code reviews, CI/CD, infrastructure automation, and production troubleshooting.
• Ability to make architectural decisions, assess tradeoffs, and improve scalability, performance, reliability, and cost.
• Experience translating requirements across Engineering, Analytics, Product, Risk, Finance, and Data Science into scalable solutions.
• Demonstrated mentoring experience and the ability to create reusable standards that help other engineers grow.
• Familiarity with AI-enabled engineering tools and an interest in data platforms for machine learning, intelligent applications, and AI workflows.
• A customer-focused, accountable, collaborative approach and a willingness to challenge existing ways of working.

Remote Work and Compensation

This is a remote-first opportunity for employees based in the United States. Working from the Manhattan office is optional.

Base pay is USD $134,000–$175,000. Actual pay reflects industry benchmarks, role function and level, compensation strategy, job-related knowledge, skills, experience, and other objective business considerations. The total package may also include a bonus and/or equity awards, separately from medical, financial, and other benefits.

Benefits and Application Information

• Flexible working practices and incentive programs.
• Unlimited paid time off, paid parental leave, and family support policies.
• Employer 401(k) matching.
• A learning and wellness subscription stipend.
• Employer-sponsored insurance for employees and dependents, including several options fully covered by the employer.
• An optional Union Square office with a casual dress code.

The employer participates in E-Verify. The application asks about current state of residence, immigration sponsorship or work authorization support, AI experience, salary expectations, and acceptance of the candidate privacy statement. Greenhouse Talent Matching assists with reviewing applications for role-related skills and experience. Equal employment opportunity and accessible recruitment practices apply; candidates may request adjustments to the hiring process.