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Senior Data Engineer

USA · Remoto160,000-195,000 USD Por año

Resumen de la oportunidad

Senior Data Engineer en Socure. Es una oportunidad remota. Ubicación publicada: USA. La información publicada no la presenta como disponible desde República Dominicana.

Empresa
Socure
Ubicación
USA
Modalidad
Remoto
Salario publicado
160,000-195,000 USD Por año
Disponibilidad desde República Dominicana
No disponible desde República Dominicana
Publicada por la fuente
22 de septiembre de 2026
Actualizada
23 de septiembre de 2026
Consultar la publicación original

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Descripción del empleo

Sobre el empleo

We are looking for a Senior Data Engineer to join our Data Automation team. You will play a critical role in designing and building scalable data platforms and pipelines that power Socure’s identity verification products and analytics. This role is ideal for someone who has a strong passion for solving real business problems with data, and combines deep hands-on data engineering expertise with strong ownership.

Requisitos

  • 5+ years of hands-on data engineering experience, building and maintaining production-grade data platforms and pipelines.
  • Strong programming skills in general-purpose language (such as Python or Scala) for data processing, and SQL for data analytics.
  • Deep experience with distributed data processing frameworks, such as Apache Spark, including performance tuning and optimization.
  • Proven experience building data solutions using services on AWS (EMR, Lambda, s3, etc).
  • Strong understanding of data modeling and data warehousing concepts, including partitioning, schema design for large-scale datasets.
  • Experience operating and supporting production pipelines, including monitoring, alerting, incident response, and improving reliability over time.
  • Solid foundation in software engineering practices, including version control, CI/CD, testing strategies, and code review.
  • Strong communication and collaboration skills, with the ability to work effectively with both technical and non-technical stakeholders.
  • Experience with streaming or near-real-time data processing (Kafka, Kinesis, etc).
  • Hands-on experience with data orchestration tools (Airflow, Step Functions, etc).
  • Familiarity with modern data platform patterns such as Data Lakehouse, Data Mesh, and large-scale data sharing across teams.
  • Experience with prompt engineering using modern GenAI, Large Language Models (LLM).
  • Experience mentoring other engineers and contributing to engineering-wide standards, best practices.
  • As a note; Socure cannot provide sponsorship now or in the future for this role.
  • Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
  • If you need an accommodation during any stage of the application or hiring process-including interview or onboarding support-please reach out to your Socure recruiting partner directly.
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Why Socure ?

Socure is building the identity trust infrastructure for the digital economy - verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.

We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself - keep reading.

What You'll Do

  • Design and build batch and streaming data pipelines to support automated data ingestion, ML feature engineering and analytics across multiple product domains.
  • Own end-to-end delivery of complex, ambiguous data initiatives, including architecture, implementation, testing, deployment, monitoring, and documentation.
  • Develop and evolve the data platform to support large-scale data processing using modern cloud-native technologies.
  • Automate data operations (validation, quality checks, alerting, backfills, and recovery workflows) to reduce manual effort and improve consistency.
  • Optimize cost, performance, and reliability of data workloads.
  • Partner closely with cross-functional teams (Data Science, Product, Engineering) to understand requirements, translate them into technical solutions.
  • Evaluate and adopt new technologies (new processing engines, storage formats, orchestration tools, GenAI-assisted ingestion) to keep the platform modern and efficient.