Top Remote Tech Jobs

Drive technical pre-sales with Account Executives: lead discovery, design tailored CDP solutions, deliver demos and proofs-of-concept, advise on architecture and streaming/data ecosystem adoption, and translate customer feedback into product and adoption initiatives.

Analyzes card fraud threats, transaction data, losses, rules, and suspicious activity to identify fraud savings and recommend control improvements. Develops monitoring and reports, supports data automation, database maintenance, quality audits, data governance, model governance, and management presentations. Collaborates with fraud, data governance, and model governance teams while monitoring fraud and analytics trends.

As a Cloudera Solutions Engineer, you will create customer-centric solutions, lead cross-functional collaborations, and transform customer feedback into product improvements.

Develop and maintain Tableau dashboards and analytics solutions; clean and transform data using Databricks, SQL, and Python; identify automation opportunities; partner with stakeholders to translate business needs into scalable AI/analytics use cases; support client services and business development for federal clients.

Design, develop, optimize, and deploy high-performance Spark/Scala data processing applications and ETL pipelines within the Cloudera Hadoop ecosystem. Collaborate with data scientists and analysts, ensure data integrity and performance, troubleshoot Spark/Cloudera issues, and implement version control and CI/CD for Spark applications.

Design, implement, administer, and optimize large-scale Hadoop clusters (HDFS, YARN, MapReduce). Monitor, secure, troubleshoot, automate provisioning, and implement backup/disaster recovery. Collaborate with data engineers/scientists, implement ETL/streaming pipelines, and document configurations and best practices.

Design and architect large-scale distributed data solutions using Spark and Cloudera CDH. Build and optimize ETL pipelines, ensure data integrity/security, troubleshoot performance, and implement version control and CI/CD for Spark applications while collaborating with data scientists and stakeholders.

Design and develop Big Data applications using Java, Spark, and MapReduce on Cloudera/Hadoop ecosystems. Work with Hive, Impala, YARN, Kafka, and large datasets; write tests (JUnit), perform data analysis, and script in Unix/Python. Lead and manage global technology teams and follow industry best practices.

Lead architect to design and implement large-scale, distributed big data solutions using Java and Big Data technologies. Hands-on with Java 1.8+, Apache Spark, MapReduce, Hadoop/Cloudera, Hive, Impala, YARN, and Kafka. Perform data analysis, design scalable architectures, enforce testing (JUnit), and optionally use Unix/Python scripting. Manage global technology teams, apply industry best practices, and communicate effectively across stakeholders.

Lead design and implementation of high-performance Spark-based data pipelines on Cloudera Hadoop. Optimize Spark applications and platform performance, build ETL processes for structured and unstructured data, ensure data integrity/security, collaborate with stakeholders, and implement version control and CI/CD for deployments.

Design, develop, optimize, test, and deploy large-scale Spark/Scala data processing and ETL pipelines within the Cloudera ecosystem. Collaborate with data scientists and analysts, ensure data integrity and security, troubleshoot performance issues, and implement version control and CI/CD for production data solutions.

Design and implement large-scale data applications using Java, Spark, and MapReduce within Cloudera/Hadoop ecosystems. Integrate Hive, Impala, YARN, and Kafka, perform data analysis, write tests (JUnit), use scripting, and manage global technology teams following industry best practices.

Design, develop, optimize, and deploy large-scale data processing applications using Scala and Apache Spark within the Cloudera Hadoop ecosystem. Build ETL pipelines, ensure data integrity and performance, troubleshoot Spark/Cloudera issues, collaborate with data scientists and analysts, and implement version control and CI/CD for Spark applications.

Provide 24/7 production support for Hadoop applications in a Cloudera data lake: monitor SLAs, triage and remediate job failures, collaborate with upstream/downstream teams, analyze and debug shell scripts/Python/Perl, manage large SQL systems, and generate reports for senior management.

Senior Java Big Data Developer to design and build Big Data applications on Cloudera/Hadoop using Java and Spark/MapReduce. Work with Hive, Impala, YARN, Kafka; perform data analysis, unit testing, and scripting. Lead/manage global technology teams and follow industry practices and standards.

Design, develop, test, and deploy high-performance Spark/Scala data processing applications and ETL pipelines using the Cloudera Hadoop ecosystem. Optimize Spark and platform performance, ensure data integrity and security, collaborate with data scientists and analysts, troubleshoot issues, and implement version control and CI/CD for Spark applications.

Design, develop, optimize, and deploy high-performance data processing applications using Scala and Apache Spark on Cloudera CDH. Build and tune ETL pipelines integrating HDFS, Hive, Impala, HBase, and Kafka. Ensure data integrity and security, troubleshoot performance, collaborate with data scientists and analysts, and implement version control and CI/CD.

Lead design and implementation of high-performance Spark-based data processing and analytics solutions on Cloudera. Build and optimize ETL pipelines, ensure data integrity and security, troubleshoot platform performance, and implement version control and CI/CD for Spark applications while collaborating with data scientists and stakeholders.

Design, develop, test, optimize, and deploy large-scale Spark/Scala data processing and ETL pipelines on Cloudera Hadoop. Collaborate with data scientists and analysts, ensure data integrity and security, troubleshoot performance, and implement version control and CI/CD for Spark applications.

Design, develop, optimize, and deploy high-performance Spark/Scala data processing applications and ETL pipelines using the Cloudera Hadoop ecosystem. Collaborate with analysts and data scientists, ensure data integrity and security, troubleshoot performance, and implement version control and CI/CD for production Spark workloads.

Design, develop, optimize, test, and deploy large-scale Spark/Scala data processing and ETL pipelines on Cloudera Hadoop. Collaborate with data scientists and analysts, ensure data integrity and performance, troubleshoot Spark/Cloudera issues, and implement version control and CI/CD for production analytics solutions.

Design and architect large-scale distributed big data solutions using Java, Spark, and Scala. Build and optimize ETL pipelines and Spark applications on the Cloudera Hadoop ecosystem (HDFS, Hive, Impala, HBase, Kafka). Ensure data integrity, performance tuning, CI/CD for Spark apps, and collaborate with data scientists and analysts to deliver analytics solutions.

Design, develop, optimize, test, and deploy high-performance data processing applications and ETL pipelines using Apache Spark and Scala within the Cloudera ecosystem. Collaborate with data scientists and analysts, ensure data integrity and security, troubleshoot performance issues, and implement version control and CI/CD for Spark applications.

Design and architect large-scale distributed big data solutions. Develop, test, and deploy Spark/Scala applications, build ETL pipelines with Cloudera Hadoop components, optimize Spark and platform performance, ensure data integrity and security, and implement version control and CI/CD for data processing workflows while collaborating with data scientists and analysts.

Design, deploy, automate, and support Cloudera Data Platform clusters and Hadoop ecosystem services. Build Ansible automation and CI/CD for provisioning, configuration, patching, monitoring, security integrations (Kerberos/LDAP/TLS), capacity planning, disaster recovery, and on-call operations to support data ingestion, processing, and analytics workloads.