u/nilukush

Grab is using AI agents to automate analytics workflows, reducing the share of routine work handled by analysts and shortening the time required to answer business questions. The company reports that the share of mechanical tickets handled by analysts fell from 44% in February to 30% in June, covering tasks such as data preparation, alerting, and reporting.

The approach is based on a five-level autonomy model that defines how much of an analytics workflow an agent can own while retaining human oversight. At Level 3, humans frame the question and review the result while agents discover data, write and execute queries, validate results, and draft analysis. At Level 4, agents can plan and orchestrate workflows while humans review defined gates. Level 5 represents end-to-end autonomy, with humans setting objectives, quality thresholds, and escalation rules. Grab retains human accountability for metric definitions, causal interpretation, business assumptions, and final decisions.

HashiCorp has introduced tfpolicy, a new HCL-based policy-as-code framework for Terraform, now available in public beta within HCP Terraform. It is designed to simplify and modernize infrastructure governance by integrating policy creation and enforcement directly into Terraform workflows, eliminating the need for separate tools and languages.

Terraform policy is a declarative, HCL-based policy as code framework that enables platform teams to define and enforce policies using a familiar language while introducing new governance capabilities designed for modern infrastructure systems.

Spotify has introduced Random Access Parquet (RAP), a storage architecture that enables low-latency point queries directly against data stored in its data lake, allowing online services and AI applications to retrieve individual records without replicating datasets into operational databases. RAP adds an external indexing layer over Apache Parquet files, enabling interactive lookups while continuing to use the same datasets for analytics, machine learning, and online serving.

• Storage disaggregation changes Kafka economics by shifting costs from infrastructure provisioning to cloud API usage, making inefficient consumer access patterns a potentially major source of operational expense.

• When storage costs shift from shared infrastructure to per-request API charges, platform teams need client-level visibility to attribute expenses; without it, a single replay job can produce major bill spikes with little visibility into their origin.

• Kafka's legacy rebalancing protocol made dynamic consumer scaling operationally disruptive because scale events triggered group-wide processing pauses. The next-generation protocol greatly reduces this barrier, making Kubernetes-native autoscaling significantly more practical.

• Multi-tenancy in Kafka has historically forced a costly trade-off: either run a dedicated cluster per team or accept weak isolation on a shared one; virtual clusters propose a middle path that delivers strict tenant boundaries without infrastructure duplication.

• Kafka has traditionally coupled partition count to consumer parallelism. Share Groups break this constraint, letting teams scale consumers independently without costly re-partitioning of topics.

Hi,

I live in UAE. I am an ex-CTO with experience in:

• Fintech
• e-commerce and marketplaces
• Social Media / Creator
• B2C
• B2B2C

And:

• Frontend (React, React Native, Expo)
• Backend (Java, Node, Python, Golang)
• AI / ML
• Devops (AWS, GCP, Terraform, Ansible)
• Data Engineering (PySpark)
• Data Analysis (Metabase)
• QA and Automation QA (Playwright, Selenium, Appium)
• Redis, Elasticsearch, Vector Search, RAG, RabbitMQ

Hiring practices in UAE are the worst but even then, I feel that something is not right beyond the poor and worst hiring practices in UAe.

I have tried to land a role since 10 months but maybe my resume still is not up to the mark. Can you help share feedback on my resume?