Senior Data Platform Engineer — Cloudera, AWS & Kubernetes The mission We are looking for a battle-tested Data Platform Engineer who can build, operate, troubleshoot, and evolve large-scale data platforms across on-premises Cloudera environments and cloud-native AWS/Kubernetes architectures. This is not a dashboard or SQL-only role. You will work where distributed compute, storage, networking, Kubernetes, and production data pipelines meet.

You must be comfortable tracing a failed workload from the application layer through Spark or Flink, Kubernetes operators, HDFS/Hive, infrastructure, and AWS services. The goal is to help us evolve safely from on-premises, VM, and EC2-based platforms into resilient, observable, cloud-native data systems. What you'll do - Build and operate production data platforms across Cloudera on premises, Cloudera cloud environments, AWS EMR, and Kubernetes.

- Design batch and streaming pipelines using Spark, Flink, Hive, and related technologies.
- Transform large datasets through filtering, sorting, joining, aggregation, partitioning, enrichment, and restructuring.
- Work with Parquet, Avro, JSON, CSV, and other delimited or semi-structured formats.
- Design storage, partitioning, compression, retention, and lifecycle strategies across HDFS, Hive, and object storage.
- Design and maintain Hive schemas, tables, partitions, metadata, and data models.
- Administer Cloudera clusters, including installation, upgrades, configuration, scaling, patching, security, backup, and recovery.
- Troubleshoot unhealthy services, failed jobs, resource contention, data skew, small-file problems, metadata issues, and storage bottlenecks.
- Tune Spark and Flink workloads for memory, CPU, parallelism, shuffle behavior, checkpointing, and recovery.
- Operate AWS services such as EMR, S3, IAM, EC2, EKS, CloudWatch, KMS, and supporting networking services.
- Deploy and operate data workloads on Kubernetes using operators, Helm, custom resources, and GitOps-based delivery.
- Help migrate workloads from physical or virtual machines and EC2 into AWS and Kubernetes-based platforms.
- Separate compute from storage where appropriate while accounting for performance, resilience, security, and cost.
- Build monitoring, alerting, capacity management, and operational runbooks for critical data services.
- Automate platform provisioning and configuration using Terraform, Ansible,



scripting, and CI/CD.
- Support production incidents involving failed pipelines, delayed data, cluster degradation, storage pressure, or infrastructure failure.
- Work with data engineering, infrastructure, security, and application teams to resolve problems across ownership boundaries. What we're looking for - Strong hands-on experience administering Cloudera platforms in production.
- Experience with both on-premises Cloudera and cloud-based Cloudera deployments.
- Deep working knowledge of Hadoop, HDFS, Hive, YARN, Spark, and the wider distributed-data ecosystem.
- Experience building or operating production workloads using Apache Flink.
- Strong understanding of distributed data processing, including partitioning, shuffling, serialization, checkpointing, and failure recovery.
- Experience transforming large datasets using joins, aggregations, filtering, sorting, and schema evolution.
- Practical knowledge of Parquet, Avro, JSON, CSV, compression formats, and serialization tradeoffs.
- Experience designing data layouts for query performance, ingestion throughput, retention, and cost.
- Strong AWS experience, particularly with EMR, S3, EC2, EKS, IAM, CloudWatch, and KMS.
- Strong Kubernetes experience, including operators, controllers, Helm, scheduling, storage, networking, and workload troubleshooting.
- Experience migrating data platforms from on-premises or VM-based environments into AWS and Kubernetes.
- Ability to troubleshoot Linux, JVM, networking, storage, DNS, certificates, and resource-management issues.
- Experience with observability platforms and the ability to correlate infrastructure symptoms with data-pipeline failures.
- Ability to automate operational work using Python, Bash, Terraform, Ansible, or equivalent tools.
- Strong judgment around production changes, data integrity, access control, rollback, and recovery. Production scenarios you should be able to handle

- A Spark job that ran in 40 minutes yesterday now takes four hours.





- A join creates severe data skew and repeatedly exhausts executor memory.
- HDFS is approaching capacity while NameNode health is degrading.
- Hive queries return incomplete results because partitions or metadata are inconsistent.
- A Flink job repeatedly fails after checkpoint recovery.
- An EMR workload is reliable but significantly more expensive than expected.
- A Kubernetes operator reports success while the underlying data workload is unhealthy.
- A migrated workload behaves differently on S3 than it did on HDFS.
- A certificate, Kerberos, IAM, DNS, or network problem presents as an application failure.
- A critical pipeline misses its SLA and ownership is unclear across platform and data teams.

Certifications Relevant certifications are useful, particularly:

- Cloudera Certified Qualified: Data Engineer - Cloudera Administrator certification or equivalent production experience - AWS data, analytics, or architecture certifications - Kubernetes certifications such as CKA or CKAD What success looks like - Data pipelines meet their reliability and processing-time objectives.
- Platform failures are detected before downstream consumers report them.
- Incidents move quickly from symptoms to an evidence-backed root cause.
- Cloudera, AWS, and Kubernetes environments are operated through repeatable automation.
- Migrations preserve data correctness while improving scalability and operability.
- Storage and compute designs balance performance, resilience, and cost.
- Data engineers can ship workloads without becoming accidental platform administrators.
- Operational knowledge becomes monitoring, automation, and runbooks—not tribal memory. The person we want You understand that a data platform is a distributed production system, not a collection of product names. You can move from a Hive execution plan to Spark executor logs, Kubernetes events, HDFS health, S3 behavior, IAM permissions, and network telemetry without losing the thread. You know the architectural differences between on-premises Hadoop and cloud- native data platforms, including where a lift-and-shift approach will fail. We need someone who can enter a degraded platform, establish the facts, protect data integrity, restore service, explain the failure chain, and make the system harder to break next time.

📌 Senior Data Engineer (Bengaluru)
🏢 Tookitaki Holding
📍 Bengaluru