CertifID

Data Engineer

Rol remoto de Data Engineering con fit claro de ubicación del candidato.

Publicado5 jul 2026

Países elegibles1 país aceptado

Señal de senioritySenior

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

India

AWS CI/CD Docker GCP Kubernetes Python Salesforce SQL

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Actualidad de la fuente5 jul 2026

Fit de ubicación1 país aceptado

Match de stackAWS, CI/CD

Camino de aplicaciónSitio de la empresa

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Match de stackAgrega skills al perfil para compararAWS, CI/CD

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Resumen del rol

Data Engineer

Requisitos y responsabilidades

Contenido del rol extraído en secciones para revisar más rápido.

What You Will Do

Design, implement, and maintain ETL/ELT pipelines that pull and merge data from multiple internal and external systems, ensuring clean, reliable, and scalable data flows across the organization.
Own the data warehouse architecture, including partitioning strategy, access controls, and security, in partnership with Security and Infrastructure teams to meet compliance and performance requirements.
Partner with data science, engineering, Revenue Operations, and executive stakeholders to define and track KPIs, translating complex requirements into reliable data solutions that drive strategic decisions.
Champion data quality and governance, ensuring insights are consistent, trustworthy, and well-documented across the organization.
Take full ownership of billing data infrastructure, including backend warehouse tables, data models, and ongoing maintenance.
Build dashboards and analytics that deliver actionable insights for executive leadership, GTM, product, and finance teams.
Share knowledge and support the growth of peers through code reviews, documentation, and informal mentorship.

Details

Design, implement, and maintain ETL/ELT pipelines that pull and merge data from multiple internal and external systems, ensuring clean, reliable, and scalable data flows across the organization.
Own the data warehouse architecture, including partitioning strategy, access controls, and security, in partnership with Security and Infrastructure teams to meet compliance and performance requirements.
Partner with data science, engineering, Revenue Operations, and executive stakeholders to define and track KPIs, translating complex requirements into reliable data solutions that drive strategic decisions.
Champion data quality and governance, ensuring insights are consistent, trustworthy, and well-documented across the organization.
Take full ownership of billing data infrastructure, including backend warehouse tables, data models, and ongoing maintenance.
Build dashboards and analytics that deliver actionable insights for executive leadership, GTM, product, and finance teams.
Share knowledge and support the growth of peers through code reviews, documentation, and informal mentorship.
5+ years of experience in data engineering or analytics engineering, ideally supporting GTM or business functions in a fast-paced environment.
Proven ability to manage data integrations across platforms such as Salesforce, HubSpot, Amplitude, Sigma, Clay, and similar tools.
Expertise in Python with strong SQL skills and experience building with a modern data stack (BigQuery, dbt, Fivetran, Airflow).
Familiarity with credit or financial data and the regulatory considerations that come with handling sensitive information.
Hands-on experience with AWS or GCP, containerization (Docker, Kubernetes), and CI/CD pipelines.
Experience building and maintaining scalable data visualization solutions (Sigma, Looker, Tableau).
Strong communication and stakeholder management skills, with the ability to partner effectively with executives and non-technical teams.
A bias toward action, comfort working in ambiguity, and a strong sense of ownership — you find answers rather than wait for direction.
Experience in fintech, cybersecurity, or fraud prevention.
Familiarity with data privacy regulations (e.g., SOC 2, GDPR, CCPA).
Contributions to open-source data tools or communities.

What You Will Bring

5+ years of experience in data engineering or analytics engineering, ideally supporting GTM or business functions in a fast-paced environment.
Proven ability to manage data integrations across platforms such as Salesforce, HubSpot, Amplitude, Sigma, Clay, and similar tools.
Expertise in Python with strong SQL skills and experience building with a modern data stack (BigQuery, dbt, Fivetran, Airflow).
Familiarity with credit or financial data and the regulatory considerations that come with handling sensitive information.
Hands-on experience with AWS or GCP, containerization (Docker, Kubernetes), and CI/CD pipelines.
Experience building and maintaining scalable data visualization solutions (Sigma, Looker, Tableau).
Strong communication and stakeholder management skills, with the ability to partner effectively with executives and non-technical teams.
A bias toward action, comfort working in ambiguity, and a strong sense of ownership — you find answers rather than wait for direction.
Experience in fintech, cybersecurity, or fraud prevention.
Familiarity with data privacy regulations (e.g., SOC 2, GDPR, CCPA).
Contributions to open-source data tools or communities.