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Information Technology 🏢 Full Time ⭐️ Verified

Senior Data Engineer - New York, NY

DataFlow Systems
New York
Estimated Salary
USD 140.000 – USD 180.000
New
Live Update
17 Juli 2026
Deadline
17 Jul 2027

Job Description

We are seeking a visionary Senior Data Engineer to join our elite team in the heart of New York City. At DataFlow Systems, we are revolutionizing how businesses process and utilize big data. You will work on scalable architectures that power our next-generation analytics platforms. If you are passionate about data integrity, cloud infrastructure, and solving complex engineering challenges, we want to hear from you.

Why Join Us?

  • Competitive salary and equity package.
  • Flexible remote and hybrid work options.
  • Comprehensive health and wellness benefits.
  • Professional development and conference budget.

Responsibilities

  • Design, build, and maintain scalable data pipelines and ETL processes using cloud-native technologies.
  • Collaborate with data scientists and product managers to translate business requirements into technical solutions.
  • Optimize data storage solutions to ensure high availability and performance under heavy loads.
  • Implement best practices for data security, governance, and compliance (GDPR/CCPA).
  • Mentor junior engineers and conduct code reviews to maintain high engineering standards.
  • Conduct root cause analysis for data anomalies and implement long-term fixes.

Qualifications

  • 5+ years of experience in Data Engineering or a related field.
  • Proficiency in programming languages such as Python, Java, or Scala.
  • Strong experience with big data technologies (e.g., Apache Spark, Hadoop, Kafka).
  • Demonstrated expertise in SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, DynamoDB).
  • Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
  • Bachelor’s degree in Computer Science, Engineering, or a related field.

Required Skills

Python SQL Apache Spark AWS Kafka Docker Kubernetes Hadoop PostgreSQL Data Pipelines

Ready to Take This Challenge?

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