Big Data Architect (MapR, Hadoop)

Key Responsibilities:

• 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.

Qualifications and Requirements:

• 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.

Desirable Skills and Certifications:

• 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.