Big Data Engineer

Vision Technologies, LLC

Sunnyvale, CA, United States

12 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

5 years minimum

Compensation

$90,000.0 - $110,000.0

Working hours

Regular working hours

Job source

www.careerbuilder.com

Tech stack

Java (Programming Language) Artificial Intelligence Airflow Amazon Web Services Apache HTTP Server Microsoft Azure Big Data Cloud Computing Continuous Delivery Continuous Integration Information Engineering Data Governance

+33 more

Data Stores Database Queries Software Debugging Distributed Systems Fault Tolerance Apache Hadoop Hadoop Distributed File System Apache HBase Apache Hive Python (Programming Language) Machine Learning NoSQL Apache Oozie Software Engineering SQL Databases Sqoop Data Streaming Unstructured Data Data Processing Scripting Apache Spark Hdinsight Electronic Medical Records Kubernetes Information Technology Collibra Apache Flink Apache Kafka Spark Streaming Data Management Amazon Elastic Mapreduce (EMR) Databricks Programming Languages

Requirements

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.Required Qualifications

Bachelor’s degree in Computer Science, Engineering, or a related technical discipline.
Five or more years of professional experience designing and operating big-data pipelines on Hadoop.
Strong hands-on expertise with Apache Spark (Scala, Python, or Java) in production environments.
Solid experience with Hive, HDFS, Sqoop, HBase, and the broader Hadoop ecosystem.
Hands-on experience with streaming data platforms such as Kafka, Spark Streaming, or Flink.
Strong SQL skills and experience working with both relational and NoSQL data stores.
Experience with workflow orchestration tools such as Airflow or Oozie.
Solid understanding of distributed systems concepts, including partitioning, replication, and fault tolerance.
Strong scripting skills in Python or Shell.
Excellent troubleshooting, debugging, and documentation skills.

Preferred Qualifications

Experience operating Hadoop on cloud platforms such as AWS EMR, Azure HDInsight, or Databricks.
Familiarity with modern lakehouse formats (Delta, Iceberg, Hudi).
Exposure to data governance tooling such as Apache Atlas or Collibra.
Experience with Kubernetes-based data platforms (Spark-on-K8s, Trino).
Hands-on experience with CI/CD and infrastructure-as-code in data engineering workflows., Amazon Web Services (AWS), Apache, Apache HBase, Apache Hadoop, Apache Hive, Apache Spark, Apache Sqoop, Artificial Intelligence (AI), Big Data, Cloud Computing, Computer Science, Consulting, Continuous Deployment/Delivery, Continuous Integration, Data Management, Data Processing, Debugging Skills, Distributed Computing, Documentation, EAD, Ecosystems, Electronic Medical Records, HDFS (Hadoop Distributed File System), High Tech Industry, Identify Issues, Java, Machine Learning, Machine Tool, Microsoft Windows Azure, NoSQL, Production Systems, Python Programming/Scripting Language, Replication and Remote Mirroring, SQL (Structured Query Language), Scala Programming Language, Software Development, Software Engineering, Structured Data, Unstructured Data