electrical engineer

civil engineer

project engineer

qa/qc engineer

mechanical engineer

big data engineer

Maaloomatiia Academy

<div>
 
 Job Title
 
 
 
 
 Cloudera Data Engineer
 
 
 
 
 Job Scope
 
 
 
 We are seeking an experienced
 
 Cloudera Data Engineer
 
 with strong hands-on expertise in
 
 Cloudera CDP, Hadoop, Spark, Hive, and HDFS
 
 . The ideal candidate will be responsible for building and maintaining big data pipelines, optimizing data workflows, and supporting production environments to ensure reliability and performance.
 
 
 
 Main Duties And Responsibilities
 
 
 
 <ul>
 <li>
 Design, build, and maintain big data pipelines using Cloudera CDP components.
 </li>
 <li>
 Develop and optimize ETL/ELT workflows using Spark, Hive, and Hadoop ecosystems.
 </li>
 <li>
 Manage and maintain HDFS storage, ensuring high availability and performance.
 </li>
 <li>
 Perform production support activities, including issue resolution, monitoring, and performance tuning.
 </li>
 <li>
 Work with data scientists, analysts, and other engineering teams to support data-driven initiatives.
 </li>
 <li>
 Ensure data governance, security, and compliance across big data environments.
 </li>
 <li>
 Troubleshoot cluster-level issues related to resource utilization, scheduling, and configuration.
 </li>
 <li>
 Participate in solution design, architecture discussions, and technical documentation.
 
 
 
 </li>
 </ul>
 
 Position Requirements
 
 
 
 <ul>
 <li>
 Strong hands-on experience with Cloudera CDP.
 </li>
 <li>
 Proficiency in Hadoop ecosystem: HDFS, YARN, MapReduce, Hive, Impala.
 </li>
 <li>
 Strong experience working with Apache Spark (PySpark or Scala preferred).
 </li>
 <li>
 Solid understanding of distributed systems and big data architecture.
 </li>
 <li>
 Experience with production support and troubleshooting in big data environments.
 </li>
 <li>
 Strong SQL skills and experience optimizing large-scale queries.
 </li>
 <li>
 Familiarity with Linux, shell scripting, and automation tools.
 </li>
 <li>
 Experience with version control (Git) and CI/CD workflows is a plus.
 
 
 
 </li>
 </ul>
 
 Education
 
 
 
 Bachelor's degree in Computer Science or any other related field
 
 
 
 Experience
 
 
 
 <ul>
 <li>
 Experience with cloud platforms (AWS, Azure, GCP) for big data workloads.
 </li>
 <li>
 Exposure to Kafka, NiFi, Oozie, or similar data orchestration tools.
 </li>
 <li>
 Knowledge of Docker, Kubernetes, or containerized environments.
 </li>
 <li>
 Understanding of security, encryption, and data governance concepts.
 </li>
 </ul>
</div>

Cloudera Data Engineer

GSSTech Group

<div>We are looking for a highly skilled Senior Data Engineer with strong expertise in PySpark, Python, and Big Data technologies to design, build, and optimize scalable data platforms and pipelines. The ideal candidate will work closely with Data Scientists, Business Analysts, and Analytics Delivery teams to develop high-performance analytics solutions and support advanced data science initiatives. 
Requirements 
 
Key Responsibilities<ul><li>
Collaborate with Analytics Delivery Leads and Lead Data Engineers to understand business requirements and deliver impactful data solutions.</li><li>
Work closely with Data Scientists and cross-functional teams to solve complex business problems through data engineering solutions.</li><li>
Manage data onboarding, data access, and stakeholder coordination for analytics initiatives.</li><li>
Design, develop, and maintain scalable, secure, and high-performance data pipelines.</li><li>
Acquire, ingest, process, and transform large-scale structured and unstructured datasets.</li><li>
Implement data engineering best practices for building reliable and production-ready data platforms.</li><li>
Perform data wrangling, cleansing, transformation, and feature engineering for machine learning and analytics use cases.</li><li>
Design and develop modular data pipelines to generate reusable features and modeling datasets.</li><li>
Build and optimize data architectures supporting advanced analytics and machine learning workloads.</li><li>
Contribute to data platform design by selecting appropriate technologies across Big Data, SQL, and NoSQL ecosystems.</li><li>
Ensure data quality, integrity, governance, security, and scalability across the data lifecycle.</li><li>
Participate in Agile squads and collaborate effectively with stakeholders across business and technology teams.</li><li>
Contribute to enterprise data architecture strategy and roadmap aligned with business objectives.</li></ul>
Required Technical SkillsCore Data Engineering<ul><li>
Strong experience in Data Engineering and Big Data solutions.</li><li>
Expert-level proficiency in PySpark.</li><li>
Strong programming experience in Python.</li><li>
Experience building large-scale distributed data processing pipelines.</li></ul>
Data Processing &amp; ETL<ul><li>
Data Ingestion</li><li>
Data Transformation</li><li>
Data Wrangling</li><li>
Data Preparation</li><li>
Data Modeling</li><li>
Feature Engineering</li><li>
ETL/ELT Development</li><li>
Batch Processing</li><li>
Data Pipeline Development</li></ul>
Big Data Technologies<ul><li>
Apache Spark / PySpark</li><li>
Distributed Data Processing Frameworks</li><li>
Big Data Ecosystems</li></ul>
Database Technologies<ul><li>
Strong SQL expertise</li><li>
Experience with Relational Databases</li><li>
Experience with NoSQL Databases</li><li>
Data Warehousing Concepts</li></ul>
Cloud &amp; Analytics Platforms (Preferred)<ul><li>
Azure Data Platform</li><li>
AWS Data Services</li><li>
Google Cloud Data Services</li><li>
Databricks</li><li>
Hadoop Ecosystem</li></ul>
Machine Learning Data Support<ul><li>
Feature Engineering for ML Models</li><li>
Data Preparation for Analytics and AI Use Cases</li><li>
Building Modeling and Feature Tables</li><li>
Supporting Data Science Workloads</li></ul>
Engineering Best Practices<ul><li>
Software Engineering Principles</li><li>
Data Pipeline Optimization</li><li>
Data Quality Management</li><li>
Performance Tuning</li><li>
Scalability &amp; Security</li><li>
Version Control (Git)</li><li>
CI/CD Concepts</li><li>
Documentation Standards</li></ul>
Required Soft Skills<ul><li>
Strong stakeholder management and communication skills.</li><li>
Experience working with Business Analysts, Data Scientists, and Product Teams.</li><li>
Ability to translate business requirements into scalable data solutions.</li><li>
Strong analytical and problem-solving skills.</li><li>
Experience working in Agile/Scrum environments.</li><li>
Ability to work independently and within cross-functional teams.</li></ul>
Preferred Qualifications<ul><li>
Experience working in enterprise-scale data platforms.</li><li>
Experience supporting machine learning and advanced analytics initiatives.</li><li>
Exposure to cloud-native data engineering solutions.</li><li>
Experience designing modern data architectures and data lake environments.</li></ul></div>

Senior Data Engineer

Edenred

<div>Take a step forward and let Edenred surprise you.
Every day, we deliver innovative solutions to improve the life of millions of people, connecting employees, companies, and merchants all around the world.
We know there are hundred ways for you to grow. With us, you will expand your skills in a multicultural, challenging, and dynamic environment.
Dare to join Edenred and get ready to thrive in a global company that will offer you endless opportunities.
Edenred is all about meritocracy. You come as you are, and you contribute. Indeed, the Edenred Group recognizes, recruits and develops all talents and singularities.
We are committed to preventing all forms of discrimination and to providing all our candidates with equal opportunities regardless of their gender and gender expression, disability, origin, religious belief and sexual orientation or any other criteria.
<ul><li>The Role
</li></ul>We&rsquo;re looking for a Data Engineer Lead to architect, build, and scale the next generation of our data platform from the ground up. This role is ideal for someone who thrives in highimpact‑ environments, enjoys solving complex technical challenges, and wants to shape the long‑term data strategy of a fast‑growing business. You&rsquo;ll work closely with engineering, product, analytics, and business teams to design modern data foundations that enable reliable insights, advanced analytics, and innovation across the organization. This is a unique opportunity to own the full lifecycle of our data ecosystem and influence how data powers decision‑making at scale.
<ul><li>Responsibilities
 
</li></ul><ul><li>Lead the end‑to‑end design, development, and scaling of a modern data platform on Azure and Databricks.
</li><li>Build, optimize, and maintain robust data pipelines and architectures supporting analytics, reporting, and advanced data products.
</li><li>Define and enforce best practices for data modeling, governance, quality, and security across all environments.
</li><li>Partner with cross‑functional teams to understand business needs and translate them into scalable technical solutions.
</li><li>Mentor and guide data engineers, fostering strong engineering standards, code quality, and operational excellence.
</li><li>Oversee platform performance, reliability, and cost efficiency, proactively identifying areas for improvement.
</li><li>Drive continuous innovation by evaluating new tools, technologies, and architectural approaches.
</li></ul><ul><li>YOUR PROFILE
 </li></ul><ul><li>7+ years of experience in data engineering, including at least 3 years in a senior or lead role.
</li><li>Proven experience designing and building a data platform from scratch in Azure and Databricks environments.
</li><li>Deep expertise in Azure Data Lake Storage, Databricks, Unity Catalog, Delta Lake, and Azure Data Factory (Certified Data Engineer by Azure/Certified Data Engineer Databricks is a plus)
</li><li>Strong proficiency in Python, PySpark, and SQL, with handson experience processing largescale datasets.
 </li><li>Solid understanding of data modeling, Medallion Architecture, and bestinclass data governance practices.
</li><li>Experience leading technical teams, setting engineering standards, and mentoring others.
</li><li>Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Statistics,Mathematics, or a related field&mdash;or equivalent practical experience.
</li><li>A collaborative, proactive problemsolver with strong communication skills and the ability to work effectively across diverse teams.
</li></ul><ul><li>REPORTING STRUCTURE:
</li></ul>The selected candidate will report directly to the Data Manager who is responsible for ensuring that projects and applications are managed appropriately.
<ul><li>The Data Engineer Lead will work closely with engineering, product, IT, and business stakeholders to design and scale the data platform that underpins analytics, reporting, and advanced data products across the organization. This role requires strong partnership with data analysts, data scientists, and domain experts to ensure data solutions are aligned with business needs and deliver measurable impact. The selected candidate will report directly to the Data Manager and will play a key role in guiding technical decisions, influencing data strategy, and ensuring that data initiatives are executed effectively and collaboratively.
</li></ul>Apply now and Vibe with Us!</div>

Data Engineer Lead

NETbuilder

<div>
 
 We&rsquo;re a global tech consultancy with over 20 years of experience delivering digital transformation across financial services, government, and commercial sectors. We offer a supportive environment where your voice is heard, your growth is supported, and your career path is tailored to you.
 
 
 
 
 
 
 
 
 As a
 
 Data Pipeline Engineer
 
 you will work on projects to help our clients operate successfully. You will collaborate with diverse stakeholders, including end-users, business analysts, designers, and operations teams to solve complex problems and execute 'hands-on' engineering tasks.
 
 
 
 
 
 
 Key Responsibilities:
 
 
 
 
 
 <ul>
 <li>
 Solution Design &amp; Implementation: Design, implement, and maintain solutions.
 </li>
 <li>
 Data Onboarding: Onboard complex data sources.
 </li>
 <li>
 Client Engagement: Manage client relationships and work directly with customers to understand their data and technology challenges.
 </li>
 <li>
 Consultation &amp; Support: Provide pre-sales support and assist with customer training and adoption.
 </li>
 <li>
 Best Practices: Stay up to date on Cribl best practices to ensure optimal solution delivery.
 </li>
 </ul>
 
 
 
 
 
 Key Skills &amp; Qualifications:
 
 
 
 
 
 
 
 Essential:
 
 
 
 
 
 <ul>
 <li>
 Proven experience in data logging/pipeline tools such as Splunk, Cribl or Elastic.
 </li>
 <li>
 Proven experience working with clients to manage demands, expectations, and business requirements.
 </li>
 <li>
 Proficiency in data onboarding activities, including developing custom parsing functions and transformations.
 </li>
 <li>
 Experience in writing complex queries, parsers and handling data streams
 </li>
 <li>
 Ability to solve complex problems with hands-on engineering tasks.
 </li>
 <li>
 2+ years of experience with relevant data tool (i.e., Splunk, Elastic, Cribl).
 </li>
 </ul>
 
 
 
 
 
 Desirable:
 
 
 
 
 
 <ul>
 <li>
 Experience configuring and maintaining large data pipelining environments.
 </li>
 <li>
 Proficiency in scripting languages (e.g., Python, Bash) and writing regular expressions.
 </li>
 <li>
 Familiarity with containerized solutions (Kubernetes, Docker, OpenShift).
 </li>
 <li>
 Experience with cloud computing platforms (AWS, Azure, or GCP).
 </li>
 <li>
 Previous experience using PMO and ITSM tooling (ServiceNow, JIRA, Confluence).
 </li>
 </ul>
 
 
 
 
 Formal certifications (preferred) in relevant data logging/pipeline tools such as Cribl, Splunk or Elastic.
 
 
 
 
 
 
 About you:
 
 
 
 
 
 <ul>
 <li>
 Passionate about technology and technical development.
 </li>
 <li>
 Proactive approach to problem-solving with excellent analytical skills.
 </li>
 <li>
 Self-sufficient with strong written and verbal communication skills.
 </li>
 <li>
 Ability to engage with non-technical staff and collaborate effectively with peers.
 </li>
 <li>
 Strong team-playing mentality and ability to work to tight deadlines.
 </li>
 </ul>
</div>

Data Pipeline Engineer

Salt

<div>
 
 ***HIRING*** - 3 Month Contract (Extendable)
 
 
 
 
 
 
 Job Title &ndash; Data Quality Engineer (Databricks).
 
 
 
 
 Salary &ndash; AED 35,000 - 38,000 Per Month.
 
 
 
 
 Contract Length &ndash; 3 Months &ndash; Extendable.
 
 
 
 
 Location &ndash; Dubai.
 
 
 
 
 Start Date &ndash; ASAP.
 
 
 
 
 
 
 
 Role Overview
 
 
 
 The Data Quality Engineer will be responsible for designing, implementing, and operating the client's enterprise data quality framework within the Databricks platform. The role will deliver automated profiling, quality rule execution, cleansing, monitoring, remediation support, and quality reporting capabilities across 170 datasets and 1,346 prioritised Critical Data Elements (CDEs).
 
 
 
 
 
 Working closely with Data Modellers, Data Catalogue Specialists, business data owners, and platform engineers, the Data Quality Engineer will establish scalable and reusable quality controls that improve trust, accuracy, completeness, consistency, timeliness, validity, and uniqueness across the client's data estate.
 
 
 
 
 
 
 
 
 
 Key Responsibilities
 
 
 
 
 
 
 
 Databricks Platform Configuration and Administration
 
 
 <ul>
 <li>
 
 Configure and manage the Databricks environment supporting enterprise data quality operations.
 
 </li>
 <li>
 Establish and maintain:
 </li>
 <li>
 Compute clusters.
 </li>
 <li>
 PySpark notebook frameworks.
 </li>
 <li>
 Delta Lake storage structures.
 </li>
 <li>
 Unity Catalog integration.
 </li>
 <li>
 Optimise platform performance for large-scale profiling and rule execution across all in-scope datasets and CDEs.
 </li>
 <li>
 Implement development, testing, and production deployment standards for data quality assets.
 </li>
 </ul>
 
 
 
 
 
 Data Profiling and Quality Assessment
 
 
 <ul>
 <li>
 
 Design and develop AI-assisted profiling notebooks using PySpark.
 
 </li>
 <li>
 Perform baseline data quality assessments across the six quality dimensions:
 </li>
 <li>
 Completeness.
 </li>
 <li>
 Accuracy.
 </li>
 <li>
 Consistency.
 </li>
 <li>
 Validity.
 </li>
 <li>
 Timeliness.
 </li>
 <li>
 Uniqueness.
 </li>
 <li>
 Capture and analyse:
 </li>
 <li>
 Null value rates.
 </li>
 <li>
 Duplicate records.
 </li>
 <li>
 Invalid values.
 </li>
 <li>
 Format violations.
 </li>
 <li>
 Outliers.
 </li>
 <li>
 Schema drift.
 </li>
 <li>
 Produce quality profiling outputs for all prioritised CDEs and datasets.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Data Quality Rule Factory Development
 
 
 <ul>
 <li>
 
 Design and implement a reusable Data Quality Rule Factory.
 
 </li>
 <li>
 Build parameterised PySpark-based rule templates capable of supporting large-scale rule deployment.
 </li>
 <li>
 Enable automated generation and management of approximately 6,730 data quality rules without manual rule-by-rule development.
 </li>
 <li>
 Ensure rules are reusable, configurable, and maintainable across multiple datasets and domains.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Data Quality Controls and Pipeline Integration
 
 
 <ul>
 <li>
 Deploy quality rules as reusable Databricks Jobs integrated into Delta Lake processing pipelines.
 </li>
 <li>
 Embed quality controls within bronze, silver, and gold processing stages.
 </li>
 <li>
 Implement automated quality gates preventing data progression where defined thresholds are not met.
 </li>
 <li>
 Maintain rule traceability and execution history for audit and governance purposes.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Data Cleansing and Quality Improvement
 
 
 <ul>
 <li>
 Develop automated remediation and cleansing pipelines using PySpark.
 </li>
 <li>
 Implement:
 </li>
 <li>
 Standardisation routines.
 </li>
 <li>
 Data enrichment processes.
 </li>
 <li>
 Deduplication logic.
 </li>
 <li>
 Schema harmonisation controls.
 </li>
 <li>
 Deploy machine learning models managed through MLflow for:
 </li>
 <li>
 Anomaly detection.
 </li>
 <li>
 Exact duplicate detection.
 </li>
 <li>
 Fuzzy matching and duplicate identification.
 </li>
 <li>
 Ensure all AI and ML recommendations are explainable, auditable, and routed through human-in-the-loop validation processes where required.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Exception Management and Reprocessing
 
 
 <ul>
 <li>
 Design and manage exception handling processes for failed quality records.
 </li>
 <li>
 Implement quarantine Delta Lake tables serving as the Failed Record Register.
 </li>
 <li>
 Capture and maintain:
 </li>
 <li>
 Failure reason.
 </li>
 <li>
 Associated CDE.
 </li>
 <li>
 Rule reference.
 </li>
 <li>
 Processing timestamp.
 </li>
 <li>
 Resolution status.
 </li>
 <li>
 Develop reprocessing workflows to support correction and controlled re-ingestion of remediated records.
 </li>
 </ul>
 
 
 
 
 
 Data Quality Metrics and Reporting
 
 
 <ul>
 <li>
 Develop Delta Lake metric aggregation structures supporting enterprise quality reporting.
 </li>
 <li>
 Calculate and publish:
 </li>
 <li>
 Data Quality Index (DQI) scores.
 </li>
 <li>
 Dimension-level quality metrics.
 </li>
 <li>
 Rule pass/fail rates.
 </li>
 <li>
 Dataset compliance scores.
 </li>
 <li>
 SLA adherence indicators.
 </li>
 <li>
 Provide curated outputs to support Power BI quality dashboards and executive reporting.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Monitoring, Alerting and Predictive Quality Management
 
 
 <ul>
 <li>
 Configure automated quality monitoring and alerting mechanisms.
 </li>
 <li>
 Implement threshold-based notifications using:
 </li>
 <li>
 Databricks SQL Alerts.
 </li>
 <li>
 Azure Monitor integrations.
 </li>
 <li>
 Develop predictive risk scoring models to identify datasets at risk of future quality degradation.
 </li>
 <li>
 Support proactive quality management and operational intervention activities.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Root Cause Analysis and Remediation Support
 
 
 <ul>
 <li>
 Apply Databricks machine learning and pattern analysis techniques to profiling and rule execution outputs.
 </li>
 <li>
 Support AI-assisted root cause analysis across established remediation categories.
 </li>
 <li>
 Identify recurring quality issues, systemic defects, and process breakdowns.
 </li>
 <li>
 Produce prioritised remediation datasets for business and operational stakeholders.
 </li>
 <li>
 Export remediation outputs to Power BI and Excel to support:
 </li>
 <li>
 Remediation Tiering Matrix.
 </li>
 <li>
 Prioritisation Scoring Models.
 </li>
 <li>
 Governance reporting processes.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Governance, Compliance and Stakeholder Engagement
 
 
 <ul>
 <li>
 Collaborate with Data Modellers and Data Catalogue Specialists to ensure quality controls align with authoritative data definitions and metadata standards.
 </li>
 <li>
 Support DDA and DGE governance processes by producing required quality artefacts and evidence.
 </li>
 <li>
 Maintain documentation, version control, and audit trails for all quality assets, rules, models, and processes.
 </li>
 <li>
 Participate in quality reviews, governance forums, and stakeholder workshops.
 </li>
 </ul>
 
 
 
 
 
 Required Skills and Experience
 
 
 <ul>
 <li>
 Strong experience designing and implementing enterprise Data Quality frameworks.
 </li>
 <li>
 Advanced Databricks engineering experience.
 </li>
 <li>
 Strong PySpark development skills.
 </li>
 <li>
 Experience with:
 </li>
 <li>
 Delta Lake.
 </li>
 <li>
 Unity Catalog.
 </li>
 <li>
 Databricks Workflows and Jobs.
 </li>
 <li>
 Databricks SQL.
 </li>
 <li>
 Experience building scalable data validation and quality rule frameworks.
 </li>
 <li>
 Knowledge of machine learning techniques for anomaly detection and data quality monitoring.
 </li>
 <li>
 Experience using MLflow for model management and deployment.
 </li>
 <li>
 Strong understanding of data governance, metadata management, and data lifecycle processes.
 </li>
 <li>
 Experience integrating data quality metrics into reporting platforms such as Power BI.
 </li>
 <li>
 Knowledge of cloud-based data engineering and modern lakehouse architectures.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Deliverables
 
 
 <ul>
 <li>
 Configured Databricks Data Quality environment.
 </li>
 <li>
 Enterprise Data Quality Rule Factory.
 </li>
 <li>
 AI-assisted profiling notebooks and baseline assessment outputs.
 </li>
 <li>
 Automated data quality validation and gating processes.
 </li>
 <li>
 Data cleansing and remediation pipelines.
 </li>
 <li>
 Failed Record Register and reprocessing workflows.
 </li>
 <li>
 Data Quality metric aggregation tables.
 </li>
 <li>
 DQI reporting feeds and dashboard datasets.
 </li>
 <li>
 Predictive quality monitoring and alerting solutions.
 </li>
 <li>
 Root Cause Analysis and remediation support datasets.
 </li>
 <li>
 Governance, audit, and compliance artefacts supporting DDA and DGE reviews.
 </li>
 </ul>
 
 
 
 
 Regards,
 
 
 Ollie
 
 
 
 
</div>

Data Quality Engineer (Databricks)

MAGNOOS Information Systems

<div>
 
 Job Title
 
 
 
 
 Kubernetes Administrator
 
 
 
 
 Job Scope
 
 
 
 We are seeking an experienced Platform Support Engineer to support a key client engagement. The ideal candidate will have a strong background in Kubernetes operations, DevOps practices, and big data platforms, with proven ability to troubleshoot, manage, and optimize critical infrastructure. The role involves client-facing responsibilities, requiring excellent communication skills to handle discussions, resolve issues, and raise tickets as needed.
 
 
 
 Main Duties And Responsibilities
 
 
 
 <ul>
 <li>
 Troubleshooting &amp; Support: Diagnose and resolve platform issues efficiently to maintain system stability and performance.
 </li>
 <li>
 Incident Management: Raise, track, and manage tickets for any platform-related incidents or service requests.
 </li>
 <li>
 Client Interaction: Engage with client teams to discuss issues, provide updates, and ensure timely resolution.
 </li>
 <li>
 Kubernetes &amp; Container Management: Build and manage Docker images; Manage Docker Quay image registry; Handle K8S components including network policies, ingress/egress, secret and configMap management; Deploy and manage Helm charts.
 </li>
 <li>
 Python Environment Management: Setup and manage virtual environments for Python-based workloads.
 </li>
 <li>
 Big Data Components: Experience with Spark, Airflow, Hive, and HDFS; Manage queue limits for workloads.
 </li>
 <li>
 Networking: Manage DNS (static and dynamic entries), proxy settings, and environment variables.
 </li>
 <li>
 Security &amp; Access Management: Manage SSL certificates and permissions; Maintain Postgres database permissions.
 </li>
 <li>
 Object Storage: Familiarity with S3-compatible systems (e.g., Dell ECS).
 </li>
 <li>
 DevOps &amp; CI/CD: Experience with GitLab pipelines and workflows.
 </li>
 <li>
 System Services: Manage system daemons and ensure service health across platforms.
 
 
 
 </li>
 </ul>
 
 Position Requirements
 
 
 
 <ul>
 <li>
 Proven expertise in Kubernetes administration and containerization.
 </li>
 <li>
 Strong knowledge of networking concepts, including DNS, proxies, and ingress/egress.
 </li>
 <li>
 Hands-on experience with big data ecosystems and workload management.
 </li>
 <li>
 Familiarity with DevOps pipelines (GitLab) and infrastructure automation.
 </li>
 <li>
 Experience with Python virtual environments and dependency management.
 </li>
 <li>
 Knowledge of SSL management, secrets, and configuration management.
 </li>
 <li>
 Strong troubleshooting, analytical, and problem-solving skills.
 </li>
 <li>
 Excellent communication and client engagement abilities.
 
 
 
 </li>
 </ul>
 
 Education
 
 
 
 Bachelor&rsquo;s degree in Computer Science, Computer Engineering or any other related field
 
 
 
 Experience
 
 
 
 <ul>
 <li>
 Knowledge of Dell ECS or other S3 object storage solutions.
 </li>
 <li>
 Familiarity with Helm chart customization and CI/CD integration.
 </li>
 <li>
 Prior experience in enterprise-scale environments handling complex deployments.
 
 
 
 </li>
 </ul>
</div>

Kubernetes Administrator

Speechify

<div>The mission of Speechify is to make sure that reading is never a barrier to learning.

Over 50 million people use Speechify's text-to-speech products to turn whatever they're reading &ndash; PDFs, books, Google Docs, news articles, websites &ndash; into audio, so they can read faster, read more, and remember more. Speechify's text-to-speech reading products include its iOS app, Android App, Mac App, Chrome Extension, and Web App. Google recently named Speechify the Chrome Extension of the Year and Apple named Speechify its 2025 Design Award winner for Inclusivity.
Today, nearly 200 people around the globe work on Speechify in a 100% distributed setting &ndash; Speechify has no office. These include frontend and backend engineers, AI research scientists, and others from Amazon, Microsoft, and Google, leading PhD programs like Stanford, high growth startups like Stripe, Vercel, Bolt, and many founders of their own companies.

Overview

We're looking to hire for our Data side of our AI team at Speechify. This role is responsible for all aspects of data collection to support our model training operations. We are able to build high-quality datasets at petabyte-scale and low cost through a tight integration of infrastructure, engineering, and research work. We are looking for a skilled Software Engineer to join us.

What You'll Do
<ul>
<li>Be scrappy to find new sources of audio data and bring it into our ingestion pipeline</li>
<li>Operate and extend the cloud infrastructure for our ingestion pipeline, currently running on GCP and managed with Terraform.</li>
<li>Collaborate closely with our Scientists to shift the cost/throughput/quality frontier, delivering richer data at bigger scale and lower cost to power our next-generation models.</li>
<li>Collaborate with others on the AI Team and Speechify Leadership to craft the AI Team's dataset roadmap to power Speechify's next-generation consumer and enterprise products.</li>
</ul>
An Ideal Candidate Should Have
<ul>
<li>BS/MS/PhD in Computer Science or a related field.</li>
<li>5+ years of industry experience in software development.</li>
<li>Proficiency with bash/Python scripting in Linux environments</li>
<li>Proficiency in Docker and Infrastructure-as-Code concepts and professional experience with at least one major Cloud Provider (we use GCP)</li>
<li>Experience with web crawlers, large-scale data processing workflows is a plus</li>
<li>Ability to handle multiple tasks and adapt to changing priorities.</li>
<li>Strong communication skills, both written and verbal.</li>
</ul>
What we offer
<ul>
<li>A fast-growing environment where you can help shape the company and product.</li>
<li>An entrepreneurial-minded team that supports risk, intuition, and hustle.</li>
<li>A hands-off management approach so you can focus and do your best work.</li>
<li>An opportunity to make a big impact in a transformative industry.</li>
<li>Competitive salaries, a friendly and laid-back atmosphere, and a commitment to building a great asynchronous culture.</li>
<li>Opportunity to work on a life-changing product that millions of people use.</li>
<li>Build products that directly impact and support people with learning differences like dyslexia, ADD, low vision, concussions, autism, and more.</li>
<li>Work in one of the fastest-growing sectors of tech, the intersection of artificial intelligence and audio.</li>
</ul>
Think you're a good fit for this job?

Tell us more about yourself and why you're interested in the role when you apply.
 
And don't forget to include links to your portfolio and LinkedIn.

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Speechify does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.</div>

Software Engineer, Data Infrastructure & Acquisition - Dubai, United Arab Emirates

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