• Become a trusted advisor working together with our clients, from data owners and analytic users to C-level executives • Engage and lead multi-disciplinary teams to solve complex use-cases across a variety of industries • Manage high-priority accounts that can range from global leaders to emerging disruptors within our target verticals • Help establish and expand commercial offerings within our priority cloud data platform partnerships (namely AWS) • Assess existing cloud infrastructure and business processes and advise on best-in-class modern solutions • Design the cloud architecture, building data ingestion and storage protocols • Ensure best-in-class integration with modern data platforms (e.g. Snowflake, Databricks) • Work with common AWS infrastructure stack like Kinesis, IoT Core, Glue, Kafka, SageMaker, Redshift • Ensure the environment adheres to best-in-class security, governance and monitoring principles (e.g. AWS IAM, Artifact, CloudWatch, CloudTrail) • Building a repeatable services and delivery frameworks for cloud infrastructure consulting services

Role Description We are seeking a highly experienced Senior Cloudera Administrator with experience with Cloudera Machine Learning to lead and manage enterprise-scale Hadoop/Cloudera environments. The ideal candidate will have strong hands-on experience with Cloudera platforms, CML, Kubernetes and Openshift and proven leadership managing teams of data engineers, and deep expertise in building and overseeing ETL pipelines. This role requires the ability to design, understand, and communicate complex data architectures and data flows. Key Responsibilities - Platform & Cluster Management: - Administer, maintain, and optimize Cloudera Hadoop clusters (CDP or legacy Cloudera distributions). - Ensure high availability, performance tuning, capacity planning, and cluster security. - Manage upgrades, patching, monitoring, and troubleshooting of Cloudera services (HDFS, YARN, Hive, HBase, Impala, Spark, Kafka, etc.). - Data Engineering & ETL: - Oversee the design, development, and maintenance of ETL/ELT pipelines using tools such as Spark, Hive, Kafka, Airflow, NiFi, or similar. - Review and guide data ingestion, transformation, and storage strategies across batch and streaming workloads. - Ensure data quality, reliability, and scalability of pipelines. - Architecture & Design: - Design, understand, and document data storage architectures and data flow diagrams. - Collaborate with stakeholders to define end-to-end data platform architecture. - Translate business requirements into scalable, secure data solutions. - Leadership: - Lead and mentor teams of data engineers, providing technical guidance and best practices. - Set standards for development, deployment, monitoring, and documentation. - Work closely with data scientists, analysts, DevOps, and application teams. - Security & Governance: - Implement security best practices including Kerberos, Ranger, and data access controls. - Support data governance, lineage, and compliance requirements. Qualifications - 7+ years of experience in data platforms, Hadoop, or big data administration. - Deep experience with CML workspaces, projects, and runtimes. - Strong hands-on experience with Cloudera (CDH/CDP) administration. - Strong experience with containerization (Docker, Kubernetes). - Strong experience with Openshift. - Proven experience managing and mentoring data engineering teams. - Strong background in ETL pipeline design and implementation. - Expertise with Spark, Hive, HDFS, YARN, Kafka, Airflow/NiFi (or similar tools). - Ability to design and draw architecture diagrams for data storage and data flows. - Solid understanding of Linux, networking, and distributed systems. - Experience with cloud platforms (AWS, Azure, or GCP) is a strong plus. - Excellent communication and documentation skills. Nice to Have - Cloudera certification(s). - Exposure to data governance tools and metadata management. - Experience in large-scale enterprise or regulated environments. Benefits - Attractive, market-leading salary package. - Clear career advancement path with professional development opportunities. - One-year contract with Xenon7, presenting a significant opportunity for renewal.

• Act as a trusted technical advisor to the teams, customer executives, and technical leadership, translating complex technical roadmaps into clear business value and risk assessments • Define the multi-year, enterprise-wide strategy for cloud adoption, migration, and modernization, with a strong focus on GCP services and strategic interoperability with the second cloud platform (Pref AWS) • Establish and enforce global cloud governance frameworks, architectural standards, reference models, and reusable patterns for deployment, security, and operations across all environments • Drive advanced DevOps practices and accountability models • Oversee cloud cost optimization initiatives, and establish technical guardrails to ensure maximum ROI across the multi-cloud footprint. • Architect and lead the implementation of complex, large-scale solutions on GCP, with hands-on experience in leveraging services like GKE, Cloud Dataflow, BigQuery, Cloud Run, Vertex AI, and Cloud Spanner • Design and govern the integration patterns for hybrid and multi-cloud systems, focusing on low-latency, secure connectivity, and consistent Identity and Access Management (IAM) across platforms (e.g., using federated identity or centralized key management) • Define the DevSecOps and cloud security posture, ensuring that designs meet stringent regulatory (e.g., data residency) and global compliance requirements (e.g., GDPR, HIPAA) • Lead Proof-of-Concept (PoC) initiatives for emerging technologies like Generative AI (e.g., Google Vertex AI or AWS Bedrock), serverless computing, and define their eventual architecture and integration into the enterprise landscape • Architect a comprehensive visibility strategy to monitor system health and proactively resolve performance bottlenecks across complex cloud environments • Design automated governance guardrails that ensure every deployment is secure, compliant, and cost-optimized by default. • Mentor, coach, and grow a group of Senior/Lead Cloud Engineers, fostering a culture of engineering excellence, ownership, and continuous learning • Own and drive continuous improvement of the cloud architecture delivery pipeline, promoting GitOps and end-to-end automation across the development lifecycle • Collaborate with the Data & AI Team and Engineering Leads to ensure architectural decisions align with product roadmaps and business domain goals • Elevate the organization’s technical brand by authoring engineering blogs and whitepapers, and representing the company as a speaker at industry conferences and meetups.

• Own the full ingestion path from edge to cloud, ensuring robot telemetry, sensor data, and warehouse events are reliably captured, transported, and made available for downstream systems. • Design, build, and operate scalable pipelines and foundational data layers (streaming and batch) that deliver low-latency, reliable data for analytics, AI/ML, and product features. • Build and maintain ingestion pipelines from object storage (e.g., S3) into Databricks, including raw → staged → analytics-ready layers, supporting both streaming and batch workloads. • Own the reliability and CI/CD of the data warehouse and foundational data layers, enabling safe, repeatable deployment of schema changes, transformations, and infrastructure that analytics engineers depend on. • Implement observability, monitoring, and data quality checks to ensure pipeline correctness, detect failures or drift, and maintain trust in data used by Vista, Portal, and Scoutmap. • Scale and optimize multi-tenant data infrastructure, balancing performance, reliability, and cost-efficiency as Cobot’s customer base and data volume grow. • Collaborate directly with robotics, AI/ML, product, and analytics teams to translate product requirements into resilient data systems that unlock customer-facing features. • Establish and enforce best practices for data engineering, reliability, security, and CI/CD across ingestion, staging, and warehouse layers—owning the foundations while enabling analytics engineers to ship metrics, marts, and dashboards efficiently.