Job Title: Big Data Engineer
Work Location: 100% Remote
Long Term Contract
NOTE : interviews and laptop pickup must occur at one of the following San Francisco, Arlington, VA, Denver, CO, Chicago, Boston, NYC, Houston, Miami, Los Angeles, Seattle, Dallas, Minneapolis, MN, Birmingham, MI or Irvine, CA.
Top Skills Required:
- Build scalable data pipelines for AI.
- Enable feature engineering and ingestion.
- Skills: Big Data, AWS.
- Focus: data readiness.
Big Data Engineer
We are seeking a Big Data Engineer to design and build scalable data platforms that support advanced analytics and AI-driven use cases. This role focuses on data readiness, enabling efficient data ingestion, transformation, and feature engineering to power downstream applications (AI/ML exposure is a plus but not required).
Your Impact
- Build and maintain scalable, high-performance data pipelines to support large-scale data processing and analytics
- Enable data ingestion and transformation frameworks for structured and unstructured data across multiple sources
- Support feature engineering pipelines to prepare high-quality datasets for analytics and AI/ML use cases
- Ensure data quality, reliability, and availability across the data lifecycle
- Collaborate with data scientists, analysts, and engineering teams to ensure data is production-ready and accessible
- Optimize data workflows for performance, scalability, and cost-efficiency in cloud environments
- Contribute to the design of modern data architectures in AWS.
Skills & Experience
- Strong experience in Big Data technologies (e.g., Spark, Hadoop, Kafka, or similar).
- Hands-on experience with AWS data ecosystem (e.g., S3, EMR, Glue, Redshift, Lambda).
- Proficient in building ETL/ELT pipelines and data ingestion frameworks.
- Experience with data modeling, schema design, and large-scale data processing.
- Strong programming skills in Python, Java, or Scala.
- Familiarity with feature engineering workflows and data preparation for analytics/AI.
- Experience with workflow orchestration tools (e.g., Airflow) is a plus.
- Understanding of data governance, quality, and pipeline monitoring.
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