Big Data Engineer
Company: BV Teck
Location: Remote (Remote)
Salary: $100,000 - $150,000 a year
Type: Full-time
Remote: Yes
Posted: 2026-07-17
About this role
Big Data Engineer – Remote
Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.
This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.
Job Title: Big Data Engineer
Location: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
Salary Range: $100,000–$150,000 Annually
Experience Required: 6+ years
Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.
Job Summary
We are seeking an experienced Big Data Engineer to design, build, and operate large-scale data processing pipelines and analytics platforms on Hadoop and related big-data ecosystems. In this role you will be responsible for ingesting, transforming, and analyzing massive volumes of structured and unstructured data to support enterprise analytics, machine learning, and reporting workloads. The ideal candidate will combine deep technical expertise across the Hadoop ecosystem with strong software engineering fundamentals and a clear understanding of how to deliver reliable, performant, and cost-effective data platforms in production environments.
Key Responsibilities
- Design, develop, and operate end-to-end big-data pipelines on Hadoop, ingesting data from a diverse mix of relational, file-based, streaming, and API-driven sources.
- Build robust ETL/ELT workflows using Apache Spark, Hive, Pig, and Sqoop, with strong attention to data quality, idempotency, error handling, and recoverability.
- Develop high-throughput streaming data pipelines using Kafka, Spark Streaming, or Flink, and integrate them with downstream analytical and operational systems.
- Optimize Spark and MapReduce jobs through careful tuning of partitioning, ...