Hadoop Solutions Developer

Bright Vision Technologies•South Jordan, UT
•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is seeking an experienced Hadoop Solutions Developer to design, build, and operate large-scale data processing pipelines and analytics platforms using Hadoop and related big-data technologies. The role focuses on ingesting, transforming, and analyzing structured and unstructured data for enterprise analytics, machine learning, and reporting, while delivering reliable, performant, and cost-effective production data platforms. This is a full-time, direct W2 position with Bright Vision Technologies on an in-house Statement of Work engagement. It is 100% remote within the continental United States, with no third-party client, vendor, or implementation partner. C2C, 1099, and third-party arrangements are not accepted. New H1B sponsorship is unavailable, although qualified candidates who need a transfer of a currently valid H1B visa may apply. A technical coding assessment is mandatory.

Requirements

  • Bachelor’s degree in Computer Science, Engineering, or a related technical discipline.
  • At least five years of professional experience designing and operating big-data pipelines on Hadoop.
  • Strong production experience with Apache Spark using Scala, Python, or Java.
  • Experience with Hive, HDFS, Sqoop, HBase, and the broader Hadoop ecosystem.
  • Hands-on experience with streaming platforms such as Kafka, Spark Streaming, or Flink.
  • Strong SQL skills and experience with relational and NoSQL data stores.
  • Experience with workflow orchestration tools such as Airflow or Oozie.
  • Understanding of distributed systems concepts, including partitioning, replication, and fault tolerance.
  • Strong scripting skills in Python or Shell.
  • Strong troubleshooting, debugging, and documentation skills.
  • Willingness to work directly as a full-time W2 employee and contribute to in-house SOW deliverables.
  • Confidence in technical abilities and hands-on experience to complete the mandatory technical coding assessment.

Nice To Haves

  • Experience operating Hadoop on cloud platforms such as AWS EMR, Azure HDInsight, or Databricks.
  • Familiarity with modern lakehouse formats such as Delta, Iceberg, and Hudi.
  • Exposure to data governance tools such as Apache Atlas or Collibra.
  • Experience with Kubernetes-based data platforms such as Spark-on-K8s or Trino.
  • Hands-on experience with CI/CD and infrastructure-as-code in data engineering workflows.

Responsibilities

  • Design, develop, and operate end-to-end Hadoop big-data pipelines that ingest data from relational, file-based, streaming, and API-driven sources.
  • Build ETL/ELT workflows using Apache Spark, Hive, Pig, and Sqoop, emphasizing data quality, idempotency, error handling, and recoverability.
  • Develop high-throughput streaming pipelines using Kafka, Spark Streaming, or Flink and integrate them with analytical and operational systems.
  • Tune Spark and MapReduce jobs, including partitioning, memory, serialization, and skew handling, to meet SLAs cost-effectively.
  • Design and maintain data models and storage layouts on HDFS, Hive, HBase, and lakehouse formats such as Parquet, ORC, Delta, Iceberg, and Hudi.
  • Implement data governance, lineage, and quality controls in collaboration with governance and security teams.
  • Build monitoring, alerting, and logging strategies for pipelines, including job-level SLAs and proactive failure detection.
  • Partner with data scientists and analysts to provide curated, reliable, well-documented datasets.
  • Automate pipeline orchestration using Airflow, Oozie, or similar workflow engines, with clear dependency management and ownership boundaries.
  • Evaluate and adopt big-data and cloud technologies such as EMR, Databricks, Snowflake, and BigQuery when they offer meaningful improvements.
  • Lead performance reviews and architecture audits of existing pipelines and propose refactoring and optimization initiatives.
  • Document data architectures, schemas, pipeline behavior, and operational runbooks.
  • Mentor junior engineers and contribute to engineering standards and best practices.
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