Electrical Engineer

Company Description Nour ElDin ElSherif is a family-owned automotive business with over 80 years of continuous experience in the industry, spanning three generations. The company is focused on expanding its sales and aftersales facilities to better serve a growing customer base. Nour ElDin ElSherif is known for delivering reliable, on-time solutions and competitive prices, supported by a passionate and credible leadership. Team members work in an environment that values deep automotive knowledge, attentive customer service, and practical guidance to help clients make informed decisions. Role Description The Automotive Engineer role at Nour ElDin ElSherif is a full-time, on-site position based in Alexandria. The Automotive Engineer will be responsible for diagnosing vehicle issues, performing technical inspections, and supporting maintenance and repair activities in alignment with company standards. Daily tasks include analyzing mechanical and electronic systems, preparing technical reports, and collaborating with service teams to ensure high-quality, safe, and efficient automotive solutions. The role also involves supporting continuous improvement initiatives, advising on complex technical problems, and communicating clearly with colleagues and supervisors to coordinate workflows. The Automotive Engineer will contribute to maintaining the company’s reputation for accurate, trustworthy, and customer-focused automotive services. Qualifications Candidates should possess strong Automotive Engineering and Automotive knowledge, with practical experience in vehicle systems and diagnostics. Candidates should possess solid Analytical Skills to interpret technical data, identify root causes of issues, and propose effective solutions. Candidates should possess foundational Electronics skills related to automotive electrical and electronic systems, sensors, and control units. Candidates should possess clear and professional Communication skills to collaborate with service teams and explain technical findings in an understandable way. Relevant qualifications such as a bachelor’s degree in Automotive Engineering or a related field are preferred. Experience in workshop or service center environments, familiarity with diagnostic tools, and knowledge of safety standards are beneficial. Ability to work on-site in Alexandria, manage multiple tasks, and maintain attention to detail in a fast-paced environment. Show more

Strong in AutoCAD but still learning Revit? No problem — grow your skills with CES! 📢 WE ARE HIRING | CES CES is looking for talented engineers to join our team in Alexandria! 🔹 Available Positions: ❶ HVAC DESIGN ENGINEER ❷ MECHANICAL DESIGN ENGINEER – FIREFIGHTING ❸ MECHANICAL DESIGN ENGINEER – PLUMBING 📌 Requirements: - 1–3 years of relevant experience. (Fresh graduates are welcome to apply!). - Good knowledge of AutoCAD and Revit. - Candidates based in Alexandria or Beheira. 📍 Location: Alexandria, Egypt 📩 How to Apply: Please send your updated CV to hr.ces22@gmail.com, mentioning the job title in the email subject. Join our team and be part of exciting engineering projects! #CES #WeAreHiring #MechanicalDesignEngineer #HVAC #Firefighting #Plumbing #AutoCAD #Revit #EngineeringJobs #Alexandria Show more

Senior Data Platform Engineer Company: Intellias Education: Bachelor’s Degree Experience Level: Senior About Intellias Let’s breathe life into great tech ideas! With 3,000 people globally, Intellias is a technology company where benchmark technological solutions are born. Join our team and contribute to digitalizing the world by building innovative, scalable, and reliable technology solutions. At Intellias, people come before processes. We foster a technology-driven environment where professionals can grow, take ownership, and make a meaningful impact. Project Overview Our client is building a standardized, governed data-sharing platform that enables product services, analytics, and AI/ML systems to share and consume data securely and reliably. The Data Platform team is responsible for establishing the contracts, controls, publishing workflows, catalog metadata, lifecycle management, lineage, permissions, and runtime components required to make reusable data safe and practical at scale. This is an opportunity to shape a platform from an early stage while solving real production challenges. The client already operates production systems, a Kafka backbone, PostgreSQL databases, analytical consumers, and AI/ML use cases. The goal is to establish a consistent, developer-friendly approach to data publishing and consumption. The initial milestone focuses on platform-managed publishing tables in service-owned PostgreSQL databases, change data capture (CDC) through Debezium into Kafka, and registration in DataHub with ownership, lifecycle, lineage, and impact-analysis metadata. Over time, the platform will evolve toward self-service data products, governed materializations, durable shared state, and reusable data capabilities across operational and analytical systems. As a Senior Data Platform Engineer, you will help design and build the foundational platform services and workflows that engineering teams will use to publish, govern, and consume shared data. Responsibilities Design, develop, and operate platform services, tools, and workflows for contract-driven data publishing and consumption. Build the initial production workflow connecting platform-managed PostgreSQL publishing tables, Debezium CDC, Kafka topics, and DataHub catalog registration. Develop GitOps-style workflows for managing data contracts, schemas, ownership, lifecycle rules, compatibility policies, metadata, and runtime configurations as code. Build developer tooling, validation mechanisms, and automation that provide fast feedback before data products are published or modified. Define and implement standards for data products, publishing contracts, schema evolution, field stability, deletion semantics, access policies, lineage, and cost attribution. Operate and improve CDC infrastructure, including Kubernetes workloads, source isolation, offsets, schema history, heartbeats, signal channels, and replication-lag monitoring. Implement reliable replay, resnapshotting, recovery, and failure-handling mechanisms for data replication workflows. Integrate platform metadata with DataHub to enable data discovery, ownership transparency, lineage tracking, and impact analysis. Apply distributed systems principles to ensure reliability, idempotency, compatibility, eventual consistency, and clear ownership boundaries. Collaborate with product engineering, infrastructure, data engineering, analytics, and AI/ML teams to establish standardized, secure, and scalable data-sharing practices. Translate evolving architectural direction into incremental, production-ready solutions while making technical trade-offs explicit. Requirements Strong experience building, deploying, and operating production software systems, preferably in platform engineering, infrastructure, backend engineering, or data-intensive environments. Deep understanding of distributed systems concepts, including reliability, idempotency, replay, eventual consistency, compatibility, ownership boundaries, and operational failure modes. Practical experience with PostgreSQL or other relational databases, including schema design, migrations, transactions, replication, and performance optimization. Experience with Kafka, event streaming, change data capture (CDC), Debezium, or similar data-movement technologies. Strong software engineering skills and experience building tools or platforms used by other engineers, such as CLIs, internal developer platforms, control planes, automation, CI validation, or self-service workflows. Familiarity with Kubernetes and cloud or infrastructure platforms, including deployment, runtime isolation, observability, and operational ownership. Ability to translate ambiguous architectural requirements into practical implementation plans and deliver production-ready solutions incrementally. Strong problem-solving skills, sound technical judgment, and the ability to make informed trade-offs in evolving environments. Excellent written and spoken English communication skills, with the ability to align multiple engineering teams around technical standards, contracts, and ownership boundaries. Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or a related field. Nice to Have Experience with DataHub or other data catalog, lineage, metadata management, or data governance platforms. Familiarity with open table formats such as Apache Iceberg and analytical storage technologies, including object-store-backed data lakes, Amazon S3, BigQuery, or ClickHouse. Experience with GitHub Actions, policy-as-code, infrastructure-as-code, controllers, operators, or reconciliation-based systems. Knowledge of data contracts, schema registries, Protobuf, Avro, JSON Schema, schema compatibility validation, field-level metadata, and contract testing. Experience supporting analytics, AI/ML workloads, feature-store-style access patterns, or cross-domain product-service integrations. Proficiency in one or more programming languages, such as JVM languages, Python, TypeScript, Go, or Ruby. Why Join Intellias? At Intellias, technology takes center stage, but people always come first. We create an environment where talented professionals can take ownership, solve meaningful engineering challenges, and continuously develop their expertise. By joining this project, you will have the opportunity to: Shape a platform from the ground up: Influence architectural decisions and establish engineering standards that will support multiple teams. Solve real production challenges: Work with established systems and technologies while building a more reliable and scalable data foundation. Make a lasting technical impact: Develop reusable platform capabilities that improve how product, analytics, and AI/ML teams share data. Grow professionally: Expand your expertise in distributed systems, platform engineering, event streaming, and data governance. Collaborate with experienced engineers: Work across technical domains to deliver practical solutions with measurable value. Our customized benefits and people-first culture support your well-being, professional growth, and long-term career development. Join Intellias and help build the next generation of governed data-sharing platforms. Show more

About the Role We are looking for an Observability Engineer – Telemetry Extension & ADOT Pipeline to build and extend telemetry capabilities for enterprise AI and cloud-native platforms. In this role, you will focus on designing scalable observability pipelines, extending OpenTelemetry instrumentation, and developing reusable telemetry components that provide actionable insights into distributed systems. You will work closely with platform, AI, and DevOps teams to create flexible and configurable telemetry solutions that support performance monitoring, troubleshooting, distributed tracing, and operational excellence at enterprise scale. Project Overview Our customer is a multinational corporation with more than a century of history, operating in 180+ countries and serving more than 1 billion consumers worldwide. The organization is undertaking a major transformation focused on introducing a new generation of Reduced-Risk Products (RRPs) and developing an innovative digital ecosystem around IoT, eCommerce, digital marketing, and AI. Its IT platform hosts 700+ applications, creating significant requirements for scalable, reliable, and standardized engineering capabilities. Intellias supports the engineering of a comprehensive software ecosystem for a game-changing IoT product at the intersection of innovative consumer experiences and cutting-edge technology. As an Observability Engineer, you will join the Core Architecture Team and contribute to the architecture and implementation of best practices across the Digital Engineering Enterprise Platform. The platform provides engineering teams with reusable services, technologies, and practices that accelerate software development and operations while addressing common SDLC challenges and ensuring security, compliance, and operational excellence. Key Responsibilities Observability & Telemetry Engineering Design and implement observability solutions for AI platforms, agent-based systems, and distributed cloud-native applications. Instrument Python services using OpenTelemetry SDK and AWS Distro for OpenTelemetry (ADOT). Develop reusable telemetry components, SDK extensions, and instrumentation patterns. Implement custom metrics, spans, and configurable telemetry capabilities. Establish standardized telemetry patterns that can be adopted across multiple platform services. Build telemetry solutions that provide actionable insights into application performance, reliability, and behavior. ADOT & OpenTelemetry Pipelines Design, configure, and maintain ADOT Collector pipelines. Configure ADOT components including: Receivers Processors Exporters Configure telemetry collection, propagation, processing, and export across AWS environments. Develop and maintain OpenTelemetry instrumentation and extension patterns. Implement and maintain distributed tracing using OpenTelemetry and AWS X-Ray. Ensure reliable telemetry delivery across distributed services and agent workflows. AWS Observability Build and maintain telemetry export pipelines to AWS CloudWatch Metrics and AWS X-Ray. Implement CloudWatch metrics, dashboards, logs, and alerting strategies. Develop custom CloudWatch metrics and dimensions to provide meaningful operational insights. Support monitoring and troubleshooting across AWS-based distributed systems. Integrate observability capabilities with AWS AgentCore Observability and related platform services. AI & Agent Observability Establish monitoring standards for: AI agents Tool invocations Workflow execution Service-to-service communication Instrument AI services and LangChain-based applications to capture traces, metrics, and execution insights. Support observability across agent workflows and distributed AI systems. Ensure telemetry provides sufficient context for performance analysis, troubleshooting, and root-cause investigation. Collaborate with AI engineers to identify bottlenecks and optimization opportunities. Trace Context & Distributed Systems Implement and maintain W3C Trace Context and Baggage propagation across applications, APIs, and agent workflows. Ensure trace continuity across distributed services and AI agent interactions. Develop telemetry patterns that support end-to-end distributed tracing. Troubleshoot gaps in trace propagation and telemetry collection. Support reliable correlation between logs, metrics, and traces. SDK & Library Development Design and develop reusable Python SDK libraries for telemetry and observability. Establish extensibility patterns that allow platform teams and customers to customize telemetry. Develop configurable telemetry components and YAML-driven configuration schemas. Maintain clean, reusable, well-documented SDK interfaces. Build automated tests to validate telemetry behavior and configuration. Collaboration & Continuous Improvement Partner with AI engineers, platform engineers, DevOps teams, and architects to define observability requirements. Identify performance, reliability, and operational challenges and propose telemetry-driven solutions. Contribute to observability standards, platform guidelines, and engineering best practices. Create technical documentation for telemetry components, configuration, pipelines, and integration patterns. Continuously improve the scalability, reliability, and usability of the enterprise observability platform. Required Qualifications & Experience Bachelor’s degree in computer science, Software Engineering, Information Technology, or a related field. 4+ years of experience in observability, platform engineering, DevOps, or a related engineering discipline. Strong hands-on experience with OpenTelemetry and/or ADOT. Experience designing and configuring ADOT / OpenTelemetry Collector pipelines, including processors and exporters. Strong understanding of OpenTelemetry SDK instrumentation and extension patterns. Experience developing Python SDKs, libraries, or reusable components. Experience implementing custom metrics, spans, and configurable telemetry. Hands-on experience with AWS CloudWatch Metrics and AWS X-Ray. Experience designing telemetry pipelines for distributed cloud-native applications. Strong understanding of distributed tracing and telemetry propagation. Experience with W3C Trace Context and Baggage propagation. Strong understanding of YAML-based configuration and schema design. Experience with structured logging, metrics, and distributed tracing. Strong troubleshooting, analytical, and problem-solving skills. Strong English communication and collaboration skills. Nice-to-Have Qualifications Familiarity with Strands or LangGraph frameworks. Experience with AWS AgentCore Observability. Experience implementing customer-facing SDK extensibility patterns. Hands-on experience with CloudWatch custom metrics and dimensions. Experience instrumenting LangChain applications. Experience with AI agents, LLM applications, or multi-agent architectures. Knowledge of AWS Distro for OpenTelemetry deployment and operational best practices. Experience working with large-scale enterprise platforms. Familiarity with CI/CD and infrastructure-as-code practices. Key Technical Skills OpenTelemetry | AWS Distro for OpenTelemetry (ADOT) | ADOT Collector | OpenTelemetry SDK | Python | Python SDK Development | AWS CloudWatch | AWS X-Ray | Distributed Tracing | Custom Metrics | Custom Spans | Telemetry Pipelines | W3C Trace Context | Baggage Propagation | YAML Configuration | Structured Logging | AI Observability | AWS AgentCore Observability | LangChain | LangGraph | Cloud-Native Observability Why This Position? Build enterprise-scale observability: Design telemetry capabilities supporting a global digital engineering ecosystem. Work at the intersection of AI and observability: Build monitoring and tracing capabilities for AI agents, tools, workflows, and distributed services. Cutting-edge technology: Work hands-on with OpenTelemetry, ADOT, AWS X-Ray, CloudWatch, and modern AI frameworks. Reusable platform impact: Develop SDKs, telemetry components, and standards that can be adopted across the enterprise. Global scale: Support technology platforms spanning 700+ applications across 180+ countries. Architecture influence: Join the Core Architecture Team and help establish enterprise observability standards and best practices. Solve complex distributed-system challenges: Improve visibility, troubleshooting, performance, and reliability across cloud-native and AI workloads. Intellias partnership: Join through Intellias and contribute to a major global technology transformation. Education Bachelor’s degree in computer science, Software Engineering, Information Technology, or a related technical field. Show more