Hadoop Big Data Developer

Job Title: Hadoop Big Data Developer

Location: 100% Remote (Continental United States)

Position Type: In-house Bright Vision Technologies SOW engagement (no third-party client or vendor)

Experience: 5+ years

Salary: 100k - 150k

Sponsorship: No new H1B sponsorship available. H1B transfers welcomed for qualified candidates.

Employment Type: Full-time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third-party)

Engagement: Long-term, multi-year, aligned to the Bright Vision SOW delivery roadmap

Compensation: Competitive base salary commensurate with experience, plus benefits.

Employment Terms & Visa Policy

This is a 100% remote, full-time, direct W2 position with Bright Vision Technologies. This role is part of Bright Vision Technologies’ in-house Statement of Work (SOW) engagement. The client, end customer, and employer for this position is Bright Vision Technologies — there is no third-party client, vendor, or implementation partner involved. We do not engage in C2C, 1099, or third-party arrangements for this role.

Candidates must be willing to work directly as a full-time W2 employee of Bright Vision Technologies and contribute to our in-house SOW deliverables. No new H1B sponsorship is available for this role, however candidates who are currently on a valid H1B visa and require a transfer are welcome to apply. We will support H1B transfers for qualified candidates.

We are seeking an experienced Hadoop Big Data Developer 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.

• 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 Map Reduce jobs through careful tuning of partitioning, memory, serialization, and skew handling to meet demanding SLAs at minimal cost.
• Design and maintain data models and storage layouts on HDFS, Hive, HBase, and modern lakehouse formats (Parquet, ORC, Delta, Iceberg, Hudi) to balance flexibility and performance.
• Implement data governance, lineage, and quality controls in collaboration with data governance and security teams.
• Build robust monitoring, alerting, and logging strategies for big-data pipelines, including job-level SLAs and proactive failure detection.
• Partner with data scientists and analysts to deliver curated, reliable, and well-documented datasets that accelerate their work.
• Automate pipeline orchestration using Airflow, Oozie, or similar workflow engines, with clean dependency management and clear ownership boundaries.
• Continuously evaluate and adopt new technologies in the big-data and cloud ecosystem (EMR, Databricks, Snowflake, Big Query) where they offer meaningful improvements.
• Lead performance reviews and architecture audits of existing pipelines, proposing concrete refactoring and optimization initiatives.
• Document data architectures, schemas, pipeline behaviors, and operational runbooks in a way that makes the platform supportable as the team scales.
• Mentor junior engineers and contribute to the team’s engineering standards and best practices.

• Bachelor’s degree in Computer Science, Engineering, or a related technical discipline.
• Five or more years of professional experience designing and operating big-data…