Big Data Engineer

Big Data Engineer

Location : Remote ( Candidates must be available for an in-person interview or laptop pickup at one of the following locations: San Francisco, CA | Arlington, VA | Denver, CO | Chicago, IL | Boston, MA | New York, NY | Houston, TX | Miami, FL | Los Angeles, CA | Seattle, WA | Dallas, TX | Minneapolis, MN | Birmingham, MI | Irvine, CA)

Duration : 6 10 Months

Interview : In-person on any of the mentioned location

Note:

• Must be local with DL copy
• LinkedIn with location and photo (before 2023)

Position Overview

We are seeking a Big Data Engineer to design and build scalable data platforms that support advanced analytics and AI-driven use cases. The primary focus of this role is data readiness , including large-scale data ingestion, transformation, processing, and feature engineering. AI/ML experience is preferred but not mandatory.

Key Responsibilities

• Build and maintain scalable, high-performance data pipelines for large-scale data processing and analytics.
• Develop data ingestion and transformation frameworks for structured and unstructured data from multiple sources.
• Build and support feature engineering pipelines to prepare high-quality datasets for analytics and AI/ML use cases.
• Ensure data quality, reliability, availability, and consistency throughout the data lifecycle.
• Collaborate with data scientists, analysts, and engineering teams to ensure data is production-ready and easily accessible.
• Optimize data workflows for performance, scalability, and cost efficiency within AWS environments.
• Contribute to the design and implementation of modern AWS-based data architectures.

Required Skills

• Strong experience with Big Data technologies such as Spark, Hadoop, Kafka, or similar platforms.
• Hands-on experience with the AWS data ecosystem , including services such as S3, EMR, Glue, Redshift, and Lambda.
• Strong experience building ETL/ELT pipelines and data ingestion frameworks .
• Experience with data modeling, schema design, and large-scale data processing.
• Strong programming experience with Python, Java, or Scala .
• Familiarity with feature engineering and data preparation for analytics or AI/ML workloads.
• Understanding of data governance, data quality, monitoring, and pipeline reliability.
• Experience with workflow orchestration tools such as Airflow is a plus.
• AI/ML exposure or experience supporting AI data pipelines is preferred but not mandatory .