data quality engineer

Openkyber

<div>DataOps Engineer / Azure Data Engineer (Public Health Data Modernization) Location: Remote Position Type: Contract (15 months with multi-year extension options)
Prior experience health care data modernization projects for state projects is highly desired
Job Summary:
We are seeking a highly skilled DataOps Engineer to lead the operationalization of a large-scale public health data infrastructure modernization initiative. This role is critical in establishing a sustainable, staff-owned DataOps foundation to improve the quality, reliability, and usability of state-wide health data. You will work at the intersection of data engineering, quality assurance, and governance, utilizing a modern cloud stack centered on Azure DevOps, Azure Databricks, and AWS Data Lake architectures.
The ideal candidate is not just a technical expert but also a mentor who can perform hands-on co-development with internal teams to build durable organizational capacity.
Key Responsibilities:
<ul><li>Data Quality &amp; Statistical Process Control (SPC): Develop and implement governed, version-controlled downstream Data Quality (DQ) rules and validation frameworks. Establish SPC monitoring for data volumes, timeliness, and distributions, including AI-assisted anomaly detection.</li>
<li>Infrastructure &amp; Pipeline Support: Translate downstream requirements into actionable specifications for upstream ingestion teams. Maintain operational performance metrics and observability dashboards.</li>
<li>Data Governance &amp; Metadata: Build and maintain data glossaries, catalogs, and end-to-end lineage visualizations within established governance tools. Define data contract requirements for external vendors, including schema expectations and QA thresholds.</li>
<li>Analytics &amp; BI Enablement: Develop governed Power BI semantic models (Cost, Quality, Access) and standardized dashboards with integrated DQ/SPC overlays.</li>
<li>Organizational Enablement: Lead role-based training through active co-building sessions. Document all processes, runbooks, and SOPs as living Wikis within Azure DevOps to ensure long-term sustainability.</li>
</ul>Required Technical Stack:
<ul><li>Platform: Azure DevOps (Boards, Repos, Pipelines, Wikis).</li>
<li>Data Engineering: Azure Databricks and AWS Data Lake (Medallion Architecture).</li>
<li>Visualization: Power BI (DAX, RLS, semantic modeling).</li>
<li>Automation: Power Automate and SharePoint integration.</li>
<li>Architecture: Experience with native AWS services and complex data ingestion pipelines.</li>
</ul>Preferred Qualifications:
<ul><li>Certifications: Microsoft Certified: Azure Data Engineer Associate, Power BI Data Analyst Associate (PL-300), or Databricks Data Engineer Professional.</li>
<li>Domain Expertise: Prior experience with healthcare data infrastructure (e.g., APCD, Medicaid, or clinical datasets).</li>
<li>Management: DAMA CDMP, Lean Six Sigma, or Prosci Change Practitioner certifications.</li>
<li>Agile: SAFe Practitioner or Scrum Master (CSM/PSM).</li>
</ul>DataOps Engineer, Azure Data Engineer, Databricks, Azure DevOps Pipelines, Power BI Developer, Healthcare Data Analytics, AWS Data Lake, Medallion Architecture, Data Governance, Statistical Process Control, Metadata Management, Data Quality Engineering, AI Anomaly Detection, Semantic Modeling, DAX, SQL, Python, Public Health IT, Data Lineage, CDMP.
For applications and inquiries, contact: hirings@openkyber.com</div>

Azure Network Engineer

Devoteam

<div>
 
 Tech for People Unlocks the Future
 
 
 
 At Devoteam, we believe that technology, combined with strong human values, can actively drive change for the better. Discover how Tech for People is shaping the future, creating a positive impact on individuals and the world around us.
 
 
 With over 25 years of passion for technology and a presence in 18+ countries across EMEA and beyond, we are committed to leveraging innovation, expertise, and human-centric values to make a difference.
 
 
 
 Devoteam Culture &amp; Values:
 
 
 
 True innovation is born from a powerful culture, fused with meaningful values.
 
 
 
 Culture:
 
 
 
 
 Fair and courageous
 
 
 
 Everyone is treated fairly &ndash; this fuels bravery. At Devoteam, we always make fair decisions. We listen and are willing to be challenged, taking courageous decisions as a result. We help our employees to progress at every step and congratulate those who deserve it.
 
 
 
 Ambition and results
 
 
 
 Ambition is nurtured at every step &ndash; this drives results. We are ambitious entrepreneurs with a taste for performance, growth and celebrating success. Commitments are always kept as we seek to achieve profitable growth to create value and employment. We aim to bring as much value as possible to our clients, at every touchpoint.
 
 
 
 Learning and innovating
 
 
 
 Curiosity and learning are at our core &ndash; this stimulates innovation. At Devoteam, we are curious. We learn and embrace innovation constantly to meet challenges and build partnerships of excellence.
 
 
 
 Caring and sharing
 
 
 
 A caring attitude is infused into our culture &ndash; this encourages sharing. We believe in the power of teams, we promote support and collaboration.
 
 
 At Devoteam, we care about our teams and want to work in a positive, productive environment. We support the development of talent and careers,
 
 
 
 Values:
 
 
 
 <ul>
 <li>
 Respect
 </li>
 <li>
 Frankness
 </li>
 <li>
 Passion
 
 
 
 </li>
 </ul>
 
 Job Description
 
 
 
 A Data Engineer is expected to develop, monitor, and optimize data quality rules and processes to ensure reliable and accurate enterprise data.
 
 
 They identify and resolve data anomalies through profiling, cleansing, and validation workflows.
 
 
 The role ensures that business decisions are driven by complete, consistent, and high-integrity data across systems.
 
 
 
 Key Responsibilities:
 
 
 
 <ul>
 <li>
 Design, implement, and maintain data quality frameworks, rules, and processes to ensure the accuracy, completeness, and reliability of enterprise data.
 </li>
 <li>
 Collaborate with data governance, MDM, and data integration teams to enforce consistent data quality standards across systems.
 </li>
 <li>
 Conduct data profiling, anomaly detection, and validation to identify quality gaps and root causes.
 </li>
 <li>
 Develop automated data quality checks and workflows for continuous monitoring and remediation.
 </li>
 <li>
 Define and manage key data quality metrics (DQ KPIs) and build dashboards to visualize performance trends.
 </li>
 <li>
 Document data quality rules, issue logs, and remediation actions as part of the data governance framework.
 </li>
 <li>
 Work closely with business data stewards and domain owners to define data quality expectations and acceptance thresholds.
 </li>
 <li>
 Support audits, compliance reviews, and data management maturity assessments by providing DQ evidence and reports.
 </li>
 <li>
 Lead the design and deployment of scalable data quality solutions across multiple data domains and systems.
 </li>
 <li>
 Define enterprise data quality standards, policies, and rule frameworks aligned with governance strategy.
 </li>
 <li>
 Establish data quality scorecards, thresholds, and escalation workflows for high-impact issues.
 </li>
 <li>
 Conduct root cause analysis and lead remediation programs for chronic data issues.
 </li>
 <li>
 Mentor junior engineers and promote data quality best practices across teams.
 </li>
 <li>
 Participate in compliance with the local data management regulations (e.g. NDMO, NDI).
 </li>
 <li>
 Design, build, and maintain scalable, secure, and robust data pipelines for efficient data ingestion and transformation, with a strong focus on building quality checks into the infrastructure itself.
 </li>
 <li>
 Collaborate with cross-functional and leadership teams (Data Scientists, Analysts, and Business Leaders) to translate complex business requirements into technical data quality solutions.
 </li>
 <li>
 Research, evaluate, and recommend new data quality tools, technologies, and practices to enhance the reliability, security, and scalability of the data infrastructure.
 
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Information Management, or related discipline.
 </li>
 <li>
 5+ years of experience in Data Quality, Data Governance, or Data Engineering roles.
 </li>
 <li>
 Proven hands-on experience with enterprise DQ tools (e.g., Informatica DQ/IDMC, Ataccama, Talend DQ, SAS DQ).
 </li>
 <li>
 Strong expertise in data profiling, rule design, standardization, and exception management.
 </li>
 <li>
 Proficiency in SQL and scripting (Python, Shell, or Scala) for advanced data validation.
 </li>
 <li>
 Deep understanding of data governance frameworks and collaboration with data stewards.
 </li>
 <li>
 Ability to design DQ dashboards, scorecards, and KPIs to monitor performance.
 </li>
 <li>
 Strong communication skills for presenting findings and recommendations to business stakeholders.
 </li>
 <li>
 Experience mentoring junior engineers or managing small DQ workstreams.
 </li>
 <li>
 CDMP Associate Certificate and/or Informatica IDQ Certificate and/or IDMC DQ Certificate
 </li>
 <li>
 Experience defining data quality strategy and embedding DQ into enterprise data platforms (MDM, DWH, Data Lake).
 </li>
 <li>
 Understanding of data lineage, root cause analysis, and impact assessment.
 </li>
 <li>
 Knowledge of metadata management and integration with data catalogs.
 </li>
 <li>
 Familiarity with regulatory and compliance requirements (e.g., NDMO, NDI).
 </li>
 <li>
 Ability to automate DQ workflows through orchestration or CI/CD pipelines.
 
 
 
 </li>
 </ul>
 
 Additional Information
 
 
 
 
 Business Unit: Data &amp; Intelligence
 
 
 
 
 Level: Mid - Senior Level
 
</div>

Data Engineer

Coca-Cola HBC

<div>
 
 
 Department:
 
 Data, Insights &amp; Artificial Intelligence (DIAI)
 
 
 
 Location:
 
 Egypt, Bulgaria, Greece
 
 
 
 
 
 As a Product Expert for Data Control &amp; Data Quality, you will play a key role in the design, build, configuration, monitoring and continuous improvement of Coca-Cola Hellenic&rsquo;s Data Quality Management products, with a primary focus on the Data Control Center (DCC). You will work closely with the Data Quality &amp; Standards Product Manager, Business Stakeholders, Business Unit Data Stewards and IT partners (DTPS), ensuring that data quality controls, monitoring and reporting enable reliable and trusted data across the organization. This is a hands-on data engineering role requiring strong technical skills in SQL, PySpark and Azure-based data platforms and the emerging AI and GenAI techniques, combined with full ownership of deliverables from production support and the flexibility to handle ad-hoc requests and parallel priorities while translating business requirements into scalable, automated and intelligent data quality solutions.
 
 
 
 
 
 
 YOUR NEW KEY RESPONSIBILITIES
 
 
 
 
 Data Quality Controls (DCC)
 
 
 
 &bull; Lead the design and end-to-end ownership of Data Quality controls in the Data Control Center (DCC), from implementation through production support and continuous improvement
 
 
 &bull; Translate business requirements into data quality rules, including logic definition, thresholds, exception handling and reduction of false positives
 
 
 &bull; Discover the possibility of AI solutions for advanced checks and/or smart data errors remediation.
 
 
 &bull; Ensure DQ controls are aligned with agreed business definitions and standards.
 
 
 
 
 
 
 Monitoring, Operations &amp; Issue Resolution
 
 
 
 &bull; Monitor DQ performance and ensure stable DCC operations, including regular refresh cycles and system reliability.
 
 
 &bull; Troubleshoot failed checks, investigate data issues and perform root-cause analysis in collaboration with relevant stakeholders.
 
 
 &bull; Support incident resolution and contribute to continuous operational improvements.
 
 
 
 
 
 
 Continuous Improvement &amp; Enhancement
 
 
 
 &bull; Implement improvements to existing DQ checks and contribute to the continuous enhancement of DCC capabilities.
 
 
 &bull; Identify opportunities to improve automation, intelligence, scalability and reusability of controls and monitoring processes.
 
 
 &bull; Identify opportunities to automate monitoring and reduce manual effort for stakeholders.
 
 
 
 
 
 
 Reporting &amp; Dashboards
 
 
 
 &bull; Develop and maintain DQ dashboards and scorecards, enabling visibility of DQ KPIs, trends and drilldowns.
 
 
 &bull; Support stakeholders with insights and reporting to track performance and drive quality improvement actions.
 
 
 
 
 
 
 Data Standards Operationalization
 
 
 
 &bull; Support data standards operationalization by ensuring alignment between published standards and DQ checks.
 
 
 &bull; Support adoption and compliance visibility by enabling reporting and transparency on standards adherence.
 
 
 
 
 
 
 Collaboration &amp; Delivery Support
 
 
 
 &bull; Work closely with the Product Manager and business stakeholders to clarify requirements, validate solutions and ensure high usability and adoption.
 
 
 &bull; Collaborate with DTPS and vendors to deliver end-to-end solutions, including integration dependencies and technical enablement.
 
 
 &bull; Run demos and workshops when needed to support rollout, stakeholder understanding and adoption.
 
 
 
 
 
 
 Testing, Release &amp; Documentation
 
 
 
 &bull; Support release and testing activities, including UAT support and regression testing.
 
 
 &bull; Maintain basic documentation of implemented DQ checks, logic, definitions and changes.
 
 
 
 
 
 
 
 
 
 ARE THESE YOUR SECRET INGREDIENTS?
 
 
 
 &bull; 3+ years in data engineering / data science /analytics engineering / data quality engineering
 
 
 &bull; Strong SQL and Python for scalable data validation and monitoring
 
 
 &bull; Experience working with Databricks and modern data pipelines (ADF or similar)
 
 
 &bull; Strong analytical and problem-solving mindset with attention to detail and quality.
 
 
 &bull; Ability to translate business requirements into technical implementation and measurable outcomes.
 
 
 &bull; Comfortable working across multiple stakeholders in a cross-functional environment.
 
 
 &bull; Strong communication skills and ability to explain technical topics to non-technical audiences.
 
 
 &bull; Ownership mindset: proactive, reliable and continuously improving solutions.
 
 
 
 
 
 
 REQUIRED TECHNICAL SKILLS
 
 
 
 &bull; SQL
 
 
 &bull; Python
 
 
 &bull; PySpark
 
 
 &bull; Azure Databricks
 
 
 &bull; Azure Data Factory
 
 
 &bull; Power BI
 
 
 &bull; Machine Learning
 
 
 &bull; AI/ GenAI
 
 
 
 
 
 
 NICE TO HAVE
 
 
 
 &bull; Power Platform (Power Automate, etc.)
 
 
 &bull; Microsoft Fabric
 
 
 &bull; Copilot Studio
 
 
 &bull; Experience working in a Data Mesh framework and/or domain data product mindset
 
 
 &bull; Good awareness of key SAP MDG, MM, S/4HANA domains
 
 
 
 
 
 
 ABOUT YOUR NEW TEAM:
 
 
 
 We are Coca-Cola Hellenic, a growth-focused consumer goods business and strategic bottling partner of the Coca-Cola Company. We bottle, distribute and sell an unrivalled range of products in 29 markets in Europe, Africa and Eurasia. As we do, we create value for all stakeholders, support socio-economic growth and build a more positive environmental impact.
 
 
 We bring together more than 30,000 people from over 70 nationalities, coming from five continents. The diversity of our markets, from mature to emerging economies, provides a wide range of attractive opportunities for growth.
 
 
 We nurture our talents. We give opportunities to people across all functions and levels, as well as different geographies, backgrounds and education. We are willing to take a risk on the people we believe in, even if they don&rsquo;t have the perfect experience. We have faith in what every person can be.
 
 
 And although we have so much to be proud of, we always stay humble. We believe the real magic happens - for us and for you - when we OPEN UP.
 
 
 
 
 
 
 AT COCA-COLA HBC, DIVERSITY HELPS US THRIVE
 
 
 
 At Coca-Cola HBC, we are an inclusive employer that thrives on diversity. This means our environment provides equal opportunities for all, regardless of race, color, religion, age, disability, sexual orientation, or gender identity. Join us in nurturing a culture where everyone belongs and contributes to our collective success.
 
 
 
 
</div>

Apple

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other&rsquo;s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It&rsquo;s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you&rsquo;ll do more than join something - you&rsquo;ll add something. 
 
As part of Apple&rsquo;s central data annotation engineering team, you&rsquo;ll help build the next generation of AI-powered user experiences. Our work provides high-quality datasets and expert feedback to train machine learning models and evaluate their performance across all products and verticals. The team maintains and develops a global distributed system that captures insights from human domain experts and analysts worldwide, operating at a scale that few companies can match. We leverage cutting-edge human-in-the-loop algorithms to create and curate datasets through fine-tuned multi-step annotation workflows and near real-time data quality feedback signals. You&rsquo;ll tackle complex challenges in ML data quality at unprecedented scale while collaborating closely with AIML engineering teams across the company. From Siri to the Photos app, from iPhone to Vision Pro, you&rsquo;ll help bring innovative experiences to millions of users worldwide. 
 
Description 
 
This role focuses on enhancing the annotation quality capabilities of Apple's central data annotation platform. You'll work on a cloud-based system that supports machine learning model development across all Apple products. The position combines backend engineering with research and concept development to improve how we measure, monitor, and optimize annotation and ML dataset quality at scale. You'll be responsible for building systems that ensure high-quality training data while developing innovative approaches to quality assessment and feedback mechanisms. 
 
As part of a close-knit team of half a dozen engineers focused on annotation quality initiatives and embedded in an even larger team of fellow data annotation engineers, you'll collaborate on shared challenges and contribute to collective problem-solving efforts. The team works together on overlapping projects, combining individual expertise in backend and frontend engineering, statistics, and ML to advance the state of annotation quality measurement and optimization. You'll have the opportunity to learn from peers tackling similar technical problems while contributing your own insights to push the boundaries of what's possible in large-scale data quality assessment. 
 
Your contributions will directly impact the foundation of Apple's AI and machine learning capabilities, ensuring that models across the entire product ecosystem are trained on the highest quality data possible.&quot;,&quot;responsibilities&quot;:&quot;Develop and maintain backend services for annotation quality measurement and monitoring systems 
 
Design and implement statistical measures and algorithms to assess annotation quality across diverse datasets incl. both foundational model driven and traditional approaches 
 
Create automated quality feedback mechanisms that provide real-time insights to annotation teams 
 
Research and prototype new approaches for improving annotation consistency and accuracy 
 
Optimize and design efficient data pipelines for dataset/annotation quality metrics collection 
 
Preferred Qualifications 
 
5+ years of experience in data annotation, data quality, or related ML value chain roles 
 
Experience building and scaling web-based platforms for machine learning workflows 
 
Advanced knowledge of statistical methods for data quality assessment 
 
Experience with distributed systems and microservices architecture 
 
Background in annotation workflow optimization 
 
Track record of research publications or patents in data quality or machine learning 
 
Experience working in cross-functional teams with both technical and non-technical stakeholders 
 
Minimum Qualifications 
 
PhD in Computer Science, Machine Learning, Statistics, Math, Physics or related field, or 5 years of equivalent work experience 
 
3+ years of backend engineering experience with cloud-based services 
 
Proficiency in Python and/or Golang for backend development 
 
Experience with AWS services including Lambda and RDS/Aurora 
 
Strong foundation in applied statistics and machine learning fundamentals 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
Pay &amp; Benefits 
 
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $139,500 and $258,100, and your base pay will depend on your skills, qualifications, experience, and location. 
 
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. 
 
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

AIML - Applied ML Engineer, Data Generation & Verification

PROZ TECHNOLOGIES

Hello, Greetings from Proztec Qatar! I hope everything is going well with you.JOB DESCRIPTIONThe Data Engineer is responsible for designing, building, and maintaining scalable data pipelines and architectures to support data processing and analytics. The role focuses on handling large datasets, ensuring data quality, and enabling seamless integration across systems. The engineer will work closely with data analysts, data scientists, and business teams to deliver reliable and efficient data solutions.DUTIES &amp; RESPONSIBILITIES<ul><li>Design, build, and maintain scalable ETL/ELT data pipelines</li><li>Process and manage large volumes of structured and unstructured data</li><li>Develop and optimize SQL queries for data extraction and transformation</li><li>Ensure data quality, integrity, and consistency across systems</li><li>Integrate data from multiple sources including APIs, databases, and cloud platforms</li><li>Monitor and troubleshoot data pipeline performance issues</li><li>Collaborate with data analysts and data scientists to support analytics and reporting needs</li><li>Implement data governance and security best practices</li><li>Maintain documentation for data flows, models, and pipelines</li><li>Optimize data storage and processing for performance and cost efficiency</li></ul>SKILLS &amp; ABILITIES<ul><li>Strong experience in SQL and database management</li><li>Hands-on experience with ETL/ELT processes and tools</li><li>Proficiency in Python for data processing and automation</li><li>Knowledge of Big Data technologies (e.g., Hadoop, Spark)</li><li>Experience with cloud data platforms (AWS, Azure, or GCP)</li><li>Strong problem-solving and analytical skills</li></ul>POTENTIAL BACKGROUND<ul><li>Minimum 4+ years of experience in Data Engineering</li><li>Experience handling large-scale data processing and pipelines</li><li>Exposure to enterprise data architecture and analytics environments</li><li>Experience working with cloud-based data solutions is a plus</li></ul>CERTIFICATIONS (MANDATORY/PREFERRED)<ul><li>Google Professional Data Engineer &ndash; Preferred</li><li>Azure Data Engineer Associate &ndash; Preferred</li><li>AWS Data Analytics Certification &ndash; Preferred</li></ul>LANGUAGE REQUIREMENT<ul><li>Arabic or Non-Arabic Speaker</li></ul>WORK LOCATION<ul><li>Onshore &amp; Offshore</li></ul>SOFT SKILLS<ul><li>Strong analytical and problem-solving abilities</li><li>Good communication and collaboration skills</li><li>Attention to detail and data accuracy</li><li>Ability to manage multiple tasks and deadlines</li><li>Proactive and solution-oriented mindset</li></ul>If you&rsquo;re interested or have relevant experience, feel free to share your updated CV.Job Type: Full-timePay: QAR15,000.00 - QAR18,000.00 per monthWork Location: In person

ByteCrew

<div>
 
 Role Summary
 
 
 
 About the job Data Engineer
 
 
 We are seeking a Data Engineer to design, build, and operate data pipelines and warehouses powering
 
 
 enterprise analytics and reporting. The role covers ingestion, transformation, modelling, and operationalisation
 
 
 across cloud and on-prem data sources.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design and implement batch + streaming data pipelines (ETL/ELT).
 </li>
 <li>
 Build and maintain dimensional/data-warehouse models (Star/Snowflake schema, slowly-changing
 
 
 </li>
 </ul>
 dimensions).
 
 
 <ul>
 <li>
 Develop in SQL, Python, and at least one orchestrator (Airflow, Azure Data Factory, AWS Glue).
 </li>
 <li>
 Operate data quality checks, lineage, and observability (Great Expectations, Monte Carlo, or similar).
 </li>
 <li>
 Optimise warehouse performance (Snowflake, Synapse, BigQuery, Redshift).
 </li>
 <li>
 Partner with BI/analytics teams on semantic models and self-service consumption.
 </li>
 <li>
 Document pipelines, schemas, and runbooks.
 
 
 </li>
 </ul>
 
 Required Qualifications
 
 
 
 <ul>
 <li>
 Bachelor's degree in CS, Engineering, Statistics, or equivalent.
 </li>
 <li>
 4+ years building production data pipelines.
 </li>
 <li>
 Strong SQL (window functions, CTEs, query tuning) and Python.
 </li>
 <li>
 Hands-on with at least one major DW/Lakehouse: Snowflake, BigQuery, Synapse, Redshift, Databricks.
 </li>
 <li>
 Experience with at least one orchestrator: Airflow, ADF, Glue, dbt + scheduler.
 </li>
 <li>
 Familiarity with cloud object storage (S3, ADLS, GCS) and file formats (Parquet, ORC, Avro).
 </li>
 <li>
 Professional English &mdash; mandatory.
 
 
 </li>
 </ul>
 
 Preferred / Nice To Have
 
 
 
 <ul>
 <li>
 Working knowledge of Arabic is a plus.
 </li>
 <li>
 Streaming experience: Kafka, Kinesis, Event Hubs, Spark Structured Streaming.
 </li>
 <li>
 dbt Analytics Engineer or cloud data engineer certifications.
 </li>
 <li>
 Exposure to data governance/cataloguing (Purview, Unity Catalog, Collibra).
 </li>
 </ul>
</div>

Data Engineer
Lahore, Pakistan

NMDC Dredging & Marine

<div>
 
 
 Responsibilities:
 
 
 
 
 
 <ul>
 <li>
 Support the setup of physical models.
 </li>
 <li>
 Supervise the configuration and calibration of instrumentation and physical models by the laboratory operations team.
 </li>
 <li>
 Check model dimensions and tolerances.
 </li>
 <li>
 Carry out testing and the preliminary phase of data analysis.
 </li>
 <li>
 Maintain accurate laboratory records and communicate with the Principal Engineer and Senior Engineer.
 </li>
 <li>
 Assist in collating and presenting study output data.
 </li>
 <li>
 Be assigned to physical model studies and report directly to the Principal Engineer and Senior Engineer.
 </li>
 <li>
 Follow agreed procedures and quality assurance standards to ensure accurate completion of physical model studies.
 </li>
 <li>
 Adhere to Health and Safety procedures relevant to laboratory work.
 </li>
 </ul>
 
 
 
 
 
 Qualifications:
 
 
 
 
 
 <ul>
 <li>
 Master&rsquo;s Degree in Civil, Coastal, or Maritime Engineering, Physics, or Oceanography is required,
 
 or
 
 a Bachelor&rsquo;s Degree in Engineering or an equivalent qualification.
 </li>
 </ul>
 
 
</div>

Hydraulic Laboratory Graduate

Search 10000+ Data quality engineer jobs near you this May. New roles added regularly across leading industries. Apply today.

10000 data quality engineer jobs Near Me

Azure Network Engineer

Data Engineer

Data Engineer

AIML - Applied ML Engineer, Data Generation & Verification

Data Engineer

Data Engineer Lahore, Pakistan

Hydraulic Laboratory Graduate