Design, architect, and implement end-to-end big data solutions using MapR, Apache Hadoop, and associated ecosystem tools (e.g., Hive, HBase, Spark, Kafka).
Lead data platform modernization efforts including architecture reviews, platform upgrades, and migrations.
Collaborate with data engineers, data scientists, and application teams to gather requirements and build scalable, secure data pipelines.
Define data governance, security, and access control strategies in the MapR ecosystem.
Optimize performance of distributed systems, including storage and compute workloads.
Guide teams on best practices in big data development, deployment, and maintenance.
Conduct code reviews and architecture assessments.
Provide mentorship to junior engineers and technical leadership across big data initiatives.

Bachelor's or Master's degree in Computer Science, Data Engineering, or related field.
6+ years of experience in data architecture, with at least 3 years working specifically on MapR and Hadoop ecosystems.
Expertise in MapR-DB, MapR Streams, and MapR-FS.
Proficiency with big data tools: Apache Spark, Kafka, Hive, HBase, Oozie, Sqoop, Flume.
Strong programming skills in Java, Scala, or Python.
Solid understanding of distributed systems, high availability, and cluster management.
Experience with data ingestion, transformation, and ETL pipelines.
Familiarity with security controls (Kerberos, Ranger, Knox, etc.) and data governance.
Experience with CI/CD, Docker, Kubernetes is a plus.

Certifications such as:
Cloudera Certified Professional (CCP), MapR Certified, or Hortonworks HDP Certification
Exposure to cloud-based big data platforms like AWS EMR, Azure HDInsight, or GCP Dataproc.
Experience with NoSQL and real-time data streaming architectures.
Ability to communicate architectural concepts to both technical and non-technical stakeholders.

Big Data Architect (MapR, Hadoop)