Why This Role Stands Out
This hybrid Data Engineer role offers a fantastic opportunity to build and optimize scalable data pipelines using cutting-edge cloud technologies, contributing directly to SANS's analytics and machine learning initiatives. You'll thrive here if you have a strong background in Python, SQL, and cloud platforms, enjoy collaborating with diverse teams, and are eager to drive data innovation in a reputable organization. Apply now to leverage your skills and grow your career in a dynamic environment!
Quick Overview
Job Description
Please do not send your resume if you are not physically local to NYC/NJ as in-person interview is required.
Job Title - Data Engineer
Location - New York, NY (Hybrid)
Duration - 12+ Months
About the Role: We are looking for a skilled Data Engineer to design, build, and maintain scalable data pipelines and infrastructure that power our analytics and machine learning capabilities. You will work cross-functionally with data scientists, analysts, and product teams to ensure data is reliable, accessible, and performant across the organization.
Key Responsibilities:
- Design, build, and maintain robust ETL/ELT pipelines to ingest, transform, and deliver data from diverse sources.
- Develop and manage data warehouses and data lakes using cloud platforms such as AWS, Google Cloud Platform, or Azure.
- Ensure data quality, integrity, and availability through monitoring, testing, and alerting frameworks.
- Collaborate with data scientists and analysts to understand data needs and deliver optimized data models.
- Optimize query performance and storage efficiency across large-scale distributed systems.
- Define and enforce data governance best practices, including lineage, cataloging, and documentation.
- Partner with engineering teams to integrate data infrastructure with production systems.
- Continuously evaluate and adopt new tools and technologies to improve the data platform.
Required Qualifications
- 5+ years of experience in data engineering, software engineering, or a related field.
- Proficiency in Python for data processing and pipeline development.
- Strong SQL skills and experience with databases (e.g., SQL Server, Oracle, Redshift).
- Experience with big data technologies such as Apache Spark, Kafka, or Flink.
- Hands-on experience with cloud platforms (AWS, Google Cloud Platform, or Azure) and their data services.
- Familiarity with version control (Git) and CI/CD practices.
- Solid understanding of data modeling concepts (star schema, data vault, etc.).
- Strong analytical and problem-solving skills with a focus on data quality and reliability.
Nice to Have:
- Experience with dbt (data build tool) for transformation and data modeling.
- Knowledge of streaming data architectures and real-time processing.
- Familiarity with data observability tools such as Monte Carlo or Great Expectations.
- Exposure to machine learning workflows and feature engineering pipelines.
- Experience working in a fast-paced startup or high-growth environment.
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