data infrastructure engineer

Microsoft

<div><div>As Microsoft continues to push the boundaries of AI, we are on the lookout for passionate individuals to work with us on the most interesting and challenging AI questions of our time. Our vision is bold and broad &mdash; to build systems that have true artificial intelligence across agents, applications, services, and infrastructure. It&rsquo;s also inclusive: we aim to make AI accessible to all &mdash; consumers, businesses, developers &mdash; so that everyone can realize its benefits.</div><div></div><div><br>
We&rsquo;re looking for a seasoned Data Infrastructure Engineer. This role is a dynamic blend of Platform Engineering, DevOps/SRE, and Big Data Infrastructure Engineering, focused on enabling large-scale data and ML pipelines and intelligent systems. If you&rsquo;ve architected big data platforms from the ground up and are eager to apply that expertise to consumer AI, we want to hear from you.</div><div></div><div><br>
You&rsquo;ll bring:</div><ul><li>
Deep technical expertise</li><li>
A passion for automation and observability</li><li>
Fluency in distributed systems</li><li>
Creativity to design scalable solutions</li><li>
And just as importantly: empathy, collaboration, and a growth mindset</li></ul><div>
Microsoft&rsquo;s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.</div><div></div><div><br>
Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.</div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul><li>
Architect and maintain scalable, reliable, and observable Big Data Infrastructure for mission-critical AI applications.</li><li>
Champion DevOps and SRE best practices&mdash;automated deployments, service monitoring, and incident response.</li><li>
Build a self-service big data platform that empowers data and platform engineers and researchers.</li><li>
Develop robust CI/CD pipelines and automate infrastructure provisioning using Infrastructure as Code tools (Bicep, Terraform, ARM).</li><li>
Collaborate with Data Engineers, Data Scientists, AI Researchers, and Developers to deliver secure, seamless big data workflows.</li><li>
Lead technical design reviews and uphold a clean, secure, and well-documented codebase.</li><li>
Proactively identify and resolve bottlenecks in data pipelines and infrastructure.</li><li>
Optimize system performance across storage, compute, and analytics layers.</li><li>
Partner with Security teams to enhance system security (IAM, OAuth, Kerberos).</li><li>
Embody and promote Microsoft&rsquo;s values: Respect, Integrity, Accountability, and Inclusion.</li></ul><h2 class="jobSectionHeader"><b>
Qualifications</b></h2><div><b>
Required Qualifications:</b></div><ul><li>
Bachelor's Degree in Computer Science, Mathematics, Software Engineering, Computer Engineering, or related field AND 6+ years experience in big data engineering, data modeling and data pipeline engineering work<ul><li>
OR Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 4+ year(s) experience in software development, or data engineering work<ul><li>
OR equivalent experience.</li></ul></li></ul></li><li>
5+ years experience in Big Data Infrastructure, DevOps, SRE, or Platform Engineering, and hands-on experience managing and scaling distributed systems&mdash;from bare-metal to cloud-native environments.</li><li>
5+ years experience deploying containerized applications using Kubernetes and Helm/Kustomize.</li><li>
5+ years experience in scripting and automation skills using Python, Bash, or PowerShell.</li><li>
5+ years experience working with Databricks for scalable data processing and analytics.</li></ul><div></div><div><b><br>
Preferred Qualifications:</b></div><ul><li>
Proven experience with cloud-native infrastructure across Azure, AWS, or GCP.</li><li>
Hands-on expertise with modern data platforms like Databricks</li><li>
Deep understanding of data storage and processing technologies:<ul><li>
Relational &amp; NoSQL databases</li><li>
Key-value stores</li><li>
Spark compute engines</li><li>
Distributed file systems (e.g., HDFS, ADLS Gen2)</li><li>
Messaging systems (e.g., Event Hub, Kafka, RabbitMQ)</li></ul></li><li>
Proven success in CI/CD pipeline management, release automation, and production troubleshooting.</li><li>
Familiarity with security practices in infrastructure environments, including IAM, OAuth, and Kerberos administration.</li><li>
Capacity planning and incident management for large-scale big data systems.</li><li>
Solid collaboration history with Data Engineers, Data Scientists, ML Engineers, Networking, and Security teams.</li><li>
Familiarity with modern web stacks: TypeScript, Node.js, React, and optionally PHP.</li><li>
Exposure to agentic workflows, deep learning, or AI frameworks.</li><li>
Practical experience integrating LLMs (e.g., GPT-based models) into daily workflows&mdash;automating documentation, code generation, reviews, and operational intelligence.</li><li>
Solid grasp of prompt engineering techniques to design, optimize, and evaluate interactions with LLMs.</li><li>
Demonstrated ability to troubleshoot and resolve complex performance and scalability issues across infrastructure layers.</li><li>
Excellent interpersonal and communication skills, with a solid passion for mentorship and continuous learning.</li><li>
Experience applying LLMs to DevOps workflows, enhancing incident response, and streamlining cross-functional collaboration.</li></ul><div></div><div><br>
Data Engineering IC5 - The typical base pay range for this role across the U.S. is USD $139,900 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.<br>
<br>
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay
<br>
<br>
Microsoft will accept applications for the role until October 31, 2025</div><div></div><div><br>
#MicrosoftAI</div>
Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.</div>

Member of Technical Staff - Data Infrastructure Engineer

Pavago

<b>Job Title:</b> Data Engineer
<br><b>Position Type:</b> Full-Time, Remote
<br><b>Working Hours:</b> U.S. client business hours (with flexibility for pipeline monitoring and data refresh cycles)
<br><br>
<b>About the Role:</b><br>
Our client is seeking a Data Engineer to design, build, and maintain reliable data pipelines and infrastructure that deliver clean, accessible, and actionable data. This role requires strong software engineering fundamentals, experience with modern data stacks, and an eye for quality and scalability. The Data Engineer ensures data flows seamlessly from source systems to warehouses and BI tools, powering decision-making across the business.
<br><br>
<b>Responsibilities:</b><b>Pipeline Development:</b><br>
<ul>
<li>Build and maintain ETL/ELT pipelines using Python, SQL, or Scala.</li>
<li>Orchestrate workflows with Airflow, Prefect, Dagster, or Luigi.</li>
<li>Ingest structured and unstructured data from APIs, SaaS platforms, relational databases, and streaming sources.</li>
</ul>
<b>Data Warehousing:</b><br>
<ul>
<li>Manage data warehouses (Snowflake, BigQuery, Redshift).</li>
<li>Design schemas (star/snowflake) optimized for analytics.</li>
<li>Implement partitioning, clustering, and query performance tuning.</li>
</ul>
<b>Data Quality & Governance:</b><br>
<ul>
<li>Implement validation checks, anomaly detection, and logging for data integrity.</li>
<li>Enforce naming conventions, lineage tracking, and documentation (dbt, Great Expectations).</li>
<li>Maintain compliance with GDPR, HIPAA, or industry-specific regulations.</li>
</ul>
<b>Streaming & Real-Time Data:</b><br>
<ul>
<li>Develop and monitor streaming pipelines with Kafka, Kinesis, or Pub/Sub.</li>
<li>Ensure low-latency ingestion for time-sensitive use cases.</li>
</ul>
<b>Collaboration:</b><br>
<ul>
<li>Partner with analysts and data scientists to provide curated, reliable datasets.</li>
<li>Support BI teams in building dashboards (Tableau, Looker, Power BI).</li>
<li>Document data models and pipelines for knowledge transfer.</li>
</ul>
<b>Infrastructure & DevOps:</b><br>
<ul>
<li>Containerize data services with Docker and orchestrate in Kubernetes.</li>
<li>Automate deployments via CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI).</li>
<li>Manage cloud infrastructure using Terraform or CloudFormation.</li>
</ul>
<b>What Makes You a Perfect Fit:</b><br>
<ul>
<li>Passion for clean, reliable, and scalable data.</li>
<li>Strong problem-solving skills with debugging mindset.</li>
<li>Balance of software engineering rigor and data intuition.</li>
<li>Collaborative communicator who thrives in cross-functional environments.</li>
</ul>
<b>Required Experience & Skills (Minimum):</b><br>
<ul>
<li>3+ years in data engineering or back-end development.</li>
<li>Strong Python and SQL skills.</li>
<li>Experience with at least one major data warehouse (Snowflake, Redshift, BigQuery).</li>
<li>Familiarity with pipeline orchestration tools (Airflow, Prefect).</li>
</ul>
<b>Ideal Experience & Skills:</b><br>
<ul>
<li>Experience with dbt for transformations and data modeling.</li>
<li>Streaming data experience (Kafka, Kinesis, Pub/Sub).</li>
<li>Cloud-native data platforms (AWS Glue, GCP Dataflow, Azure Data Factory).</li>
<li>Background in regulated industries (healthcare, finance) with strict compliance.</li>
</ul>
What Does a Typical Day Look Like?
<br>A Data Engineer’s day revolves around keeping pipelines running, improving reliability, and enabling teams with high-quality data. You will:
<br><ul>
<li>Check pipeline health in Airflow/Prefect and resolve any failed jobs.</li>
<li>Ingest new data sources, writing connectors for APIs or SaaS platforms.</li>
<li>Optimize SQL queries and warehouse performance to reduce costs and latency.</li>
<li>Collaborate with analysts/data scientists to deliver clean datasets for dashboards and models.</li>
<li>Implement validation checks to prevent downstream reporting issues.</li>
<li>Document and monitor pipelines so they’re reproducible, scalable, and audit-ready.</li>
</ul>
<b>In essence:</b> you ensure the business has accurate, timely, and trustworthy data powering every decision.
<br><br>
<b>Key Metrics for Success (KPIs):</b><br>
<ul>
<li>Pipeline uptime ≥ 99%.</li>
<li>Data freshness within agreed SLAs (hourly, daily, weekly).</li>
<li>Zero critical data quality errors reaching BI/analytics.</li>
<li>Cost-optimized queries and warehouse performance.</li>
<li>Positive feedback from data consumers (analysts, scientists, leadership).</li>
</ul>
<b>Interview Process:</b><br>
<ul>
<li>Initial Phone Screen</li>
<li>Video Interview with Pavago Recruiter</li>
<li>Technical Task (e.g., build a small ETL pipeline or optimize a SQL query)</li>
<li>Client Interview with Engineering/Data Team</li>
<li>Offer & Background Verification</li>
</ul>


Data Engineer

Envision Employment Solutions

Envision Employment Solutions is currently looking for a Data & AI Engineer for one of our partners, a global leader in the chemical industry field.
<br><br>
<b>Job Summary:</b><br>
The Data & AI Engineer is responsible for designing, building, and maintaining the data infrastructure, pipelines, and AI solutions necessary to support the company's business needs. This role combines expertise in data engineering and applied AI to enable scalable data processing, advanced analytics, and machine learning capabilities. The engineer will work with large datasets, optimize data flows, and deliver AI-driven solutions that enhance decision-making and operational efficiency.
<br>The ideal candidate brings extensive experience in data engineering and a strong technical background in data architecture and ETL processes. They demonstrate a deep understanding of data governance and best practices, with a proven ability to manage large-scale data environments. The candidate excels in collaboration, problem-solving, and driving continuous improvement in data engineering practices.
<br><br>
<b>Responsibilities:</b><br>
<ul>
<li>Design, develop, and maintain data pipelines to support data integration, processing, and AI/ML workflows.</li>
<li>Ensure data quality, integrity, and security across all data and AI systems.</li>
<li>Develop and implement ETL processes to ingest data from diverse sources for analytics and machine learning.</li>
<li>Optimize data storage, retrieval, and AI model serving for performance and scalability.</li>
<li>Collaborate with data scientists and analysts to provide high-quality datasets, features, and infrastructure for analytics, reporting, and AI experimentation.</li>
<li>Build, deploy, and maintain AI/ML models to solve business problems, automate processes, and enhance decision-making.</li>
<li>Implement and maintain data governance, model governance, and MLOps best practices.</li>
<li>Monitor and troubleshoot data pipelines, AI models, and ML workflows to ensure reliability, accuracy, and efficiency.</li>
<li>Support data-driven and AI-driven initiatives by enabling timely access to trusted data and scalable AI solutions.</li>
<li>Develop and maintain documentation for data processes, AI pipelines, and deployed models.</li>
<li>Stay up to date with emerging data and AI technologies, tools, and industry best practices.</li>
<li>Ensure compliance with data protection regulations, ethical AI principles, and internal policies.</li>
<li>Provide training and support to stakeholders on data systems, AI capabilities, and best practices.</li>
<li>Lead efforts to continuously improve data infrastructure, AI platforms, and automation processes.</li>
<li>Collaborate with IT, business units, and functional teams to align data and AI initiatives with organizational goals.</li>
</ul>
<b>Requirements:</b><br>
<ul>
<li>Bachelor’s degree in computer science, Information Technology, Data Science, or a related field.</li>
<li>Proven experience of at least 5+ years in data engineering, 2+ years in AI engineering, with a focus on building and maintaining data & AI pipelines in a CoE or Specialist role.</li>
<li>Proven experience in developing and implementing ETL processes.</li>
<li>Strong background in data architecture and Azure technologies.</li>
<li>In-depth knowledge of data governance and data quality management principles.</li>
<li>Excellent written and verbal English language proficiency</li>
</ul>
<b>Skills:</b><br>
<ul>
<li>Demonstrated expertise in designing, maintaining, and optimizing data pipelines, AI pipelines, and infrastructure.</li>
<li>Strong technical knowledge of ETL processes, data integration, and MLOps using Microsoft Fabric or Azure tools.</li>
<li>Proficiency in programming languages (e.g., Python, Java, SQL) for data processing, as well as AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).</li>
<li>Hands-on experience in developing, deploying, and scaling machine learning models in production environments.</li>
<li>Excellent problem-solving and analytical skills, with a proactive approach to identifying and addressing data and AI challenges.</li>
<li>Strong communication and interpersonal skills, able to engage effectively with stakeholders at all levels and explain AI-driven insights in business terms.</li>
<li>Adept at managing multiple projects and priorities in a dynamic environment, including data and AI initiatives.</li>
<li>Proficiency in data governance, model governance, and data quality management principles.</li>
<li>Strong understanding of data protection regulations, compliance requirements, and ethical AI practices.</li>
<li>Experience in troubleshooting and resolving data and AI-related issues, including pipeline failures and model drift.</li>
<li>Ability to provide training and support on data systems, AI tools, and best practices.</li>
<li>Adaptable and agile mindset towards continuous improvement, staying current with evolving data, cloud, and AI technologies.</li>
<li>Strong analytical skills to interpret data, evaluate model performance, and predict trends for business advantage.</li>
<li>Ability to work closely with team members, management, senior leadership, and cross-functional teams to deliver data- and AI-driven value.</li>
<li>Excellent verbal and written communication skills in English, with the ability to articulate and present complex technical and AI concepts clearly and concisely.</li>
</ul>


Data & AI Engineer

AJEX

<div>
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 <p>
  We are seeking a skilled and detail-oriented Data Engineer to join our team. The ideal candidate will be responsible for designing, building, and maintaining scalable data pipelines and infrastructure to support data analytics and business intelligence initiatives. You will work closely with stakeholders to ensure data is clean, reliable, and accessible. This role requires a strong understanding of data warehousing principles, ETL processes and data modeling.
 </p>
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  The ideal candidate will be a critical thinker, an excellent problem-solver, and possess strong communication skills to collaborate effectively with various business stakeholders.
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    Key Responsibilities:
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  <li>
   Design, develop, and maintain robust ETL/ELT pipelines to ingest data from various sources.
  </li>
  <li>
   Design and optimize scalable, secure data architectures (e.g., data lakes, data warehouses, real-time streaming systems).
  </li>
  <li>
   Ensure data quality, integrity, and security across all systems.
  </li>
  <li>
   Monitor and troubleshoot data pipeline performance and reliability.
  </li>
  <li>
   Implement data governance best practices and ensure compliance with relevant data privacy regulations (e.g., GDPR, local laws).
  </li>
  <li>
   Automate data workflows and repetitive tasks using scripting and orchestration tools to enhance operational efficiency.
  </li>
  <li>
   Partners with cross-functional teams to understand data needs, provide access, and deliver integrated, automated solutions.
  </li>
  <li>
   Translate business requirements into technical specifications and data models.
  </li>
  <li>
   Enhance data storage and retrieval mechanisms for performance and cost-efficiency.
  </li>
  <li>
   Maintain documentation for data systems and processes.
  </li>
  <li>
   Contribute to the development of CI/CD pipelines for data infrastructure.
  </li>
  <li>
   Evaluate and integrate new tools and technologies to improve data engineering capabilities.
  </li>
 </ul>
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  <strong>
   Requirements
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 </p>
 <ul>
  <li>
   Bachelor&rsquo;s or master&rsquo;s degree in computer science or equivalent.
  </li>
  <li>
   Should have minimum 6+ years as Data Engineer.
  </li>
  <li>
   Strong proficiency in SQL and programming languages such as Python and Java.
  </li>
  <li>
   Must Needed Skills: Java, Data Modeling, ETL.
  </li>
  <li>
   Strong SQL skills with experience in MySQL, Mongo DB etc.
  </li>
  <li>
   Good to have: Experience dealing with other Business Intelligence Tools
  </li>
  <li>
   Ability to work independently and collaboratively within a team environment
  </li>
  <li>
   Experience in logistics, supply chain, or e-commerce domains is a plus.
  </li>
 </ul>
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</div>


Neurealm

<div><div><div><div><div><div><p>Chennai, Tamil Nadu, India</p></div></div></div><div><p><br>
</p><p>
</p></div></div><div><div><div><div>Department</div><div>
Data Science/Big Data Mining</div></div><div><div>
Job posted on</div><div>
Oct 22, 2025</div></div><div><div>
Employment type</div><div>
Full Time Employee</div></div></div></div></div><div></div><div><div><div><div><div>
A Data Engineer Job Description Samples &amp; Tips in 2025A data engineer designs, builds, and maintains the data infrastructure, such as databases and data pipelines, that collect, store, and process raw data into usable information for data scientists and business analysts.</div></div></div></div></div></div>

Sr. Databricks Engineer

Tarmeez Capital | ترميز المالية

<div>
 <strong>
  About The Role
  <br/>
  <br/>
 </strong>
 We are looking for a Data Engineer 0-1 years of experience, with knowledge or experience in data pipelines, scalable systems, and maintaining data infrastructure. In this role you&rsquo;ll be designing data pipelines, integrating with external and internal data sources, developing data products, deploying AI use-cases and maintaining them.
 <br/>
 <br/>
 Key Responsibility
 <br/>
 <br/>
 <ul>
  <li>
   Design and implement efficient ETL/ELT pipelines, with proper monitoring and alerts.
  </li>
  <li>
   Develop a data warehouse system that stores a cleaned version of various data sources from internal and external systems.
  </li>
  <li>
   Maintain data infrastructure in terms of governance, roles, and managing the cloud credit budget.
  </li>
  <li>
   Develop data quality checks to ensure the data quality and integrity is properly maintained for Data and AI use-cases.
  </li>
  <li>
   Develop scalable applications to serve data products to end-users, both for internal and external use-cases.
  </li>
  <li>
   Optimize and support team members in terms of pipeline development and dashboard deployment.
  </li>
  <li>
   Benchmark tools and specs of services to optimize cost and increase efficiency when deploying data infrastructure.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Requirements
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Saudi National
  </li>
  <li>
   Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
  </li>
  <li>
   Fresh Grad or up to 1 year of experience in Data engineering, Software Engineering, or related fields.
  </li>
  <li>
   Good knowledge of Computer Science principles and fundamentals.
  </li>
  <li>
   Understanding of ETL/ELT pipelines development and best practices.
  </li>
  <li>
   Experience with Data Visualization tools.
  </li>
  <li>
   Experience or Knowledge of Database design and managing Data Warehouse systems.
  </li>
  <li>
   Experience with SQL and Python (or other programming languages).
  </li>
  <li>
   Strong knowledge in Backend development and managing deployments.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Skills
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Developed an application in the past and designed its data models.
  </li>
  <li>
   Familiarity with Snowflake, Databricks, or similar data platforms.
  </li>
  <li>
   Experience with CDC tools and replicating from production databases.
  </li>
  <li>
   Worked on streaming use-cases using Apache Kafka or equivalent.
  </li>
  <li>
   Setup monitoring and alerts for both data pipelines and applications.
  </li>
 </ul>
</div>


Data Engineer -(Fresh Grad)- (Saudi National)

Folio3

<div><div><p>Ready to design, optimize, and maintain highly scalable data infrastructure? Folio3 is hiring a Data Engineer to lead the development of our ETL/ELT pipelines and advanced cloud data platforms.</p>
<h4 class="jobSectionHeader"><b>
Responsibilities:</b></h4>
<ul><li>Design and deploy scalable ETL/ELT pipelines using dbt, Airflow, or Azure Data Factory.</li>
<li>Develop integrations between Snowflake, Databricks, and cloud storage layers (S3, ADLS, GCS).</li>
<li>Build data models for analytics and reporting teams.</li>
<li>Implement data quality, lineage, and monitoring frameworks.</li>
<li>Optimize SQL queries, warehouse costs, and job performance.</li>
<li>Collaborate with BI/AI teams for data accessibility and consistency.</li>
<li>Enforce best practices for code versioning, CI/CD, and data governance.</li>
</ul><h4 class="jobSectionHeader"><b>Requirements:</b></h4>
<ul><li>2-5 years of progressive, hands-on experience in Data Engineering.</li>
<li>Strong SQL and Python (Pandas / PySpark).</li>
<li>Working experience with 2 or more clouds (AWS + Azure / GCP).</li>
<li>Familiar with Snowflake (Snowpipe, Streams/Tasks) or Databricks (Delta Lake, Spark).</li>
<li>Understanding of data orchestration (Airflow, dbt, Dagster).</li>
<li>Basic understanding of DevOps (Docker, GitHub Actions)</li></ul></div></div>

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