data analyst

data engineer

ai platform engineer

data scientist

devops engineer

data pipeline engineer

Atos

<div>
 
 About Atos
 
 
 
 Atos is a global leader in digital transformation with c. 78,000 employees and annual revenue of c. &euro; 10 billion. European number one in cybersecurity, cloud and high-performance computing, the Group provides tailored end-to-end solutions for all industries in 68 countries. A pioneer in decarbonization services and products, Atos is committed to a secure and decarbonized digital for its clients. Atos is a SE (Societas Europaea) and listed on Euronext Paris.
 
 
 The purpose of Atos is to help design the future of the information space. Its expertise and services support the development of knowledge, education and research in a multicultural approach and contribute to the development of scientific and technological excellence. Across the world, the Group enables its customers and employees, and members of societies at large to live, work and develop sustainably, in a safe and secure information space.
 
 
 
 Senior Data Engineer
 
 
 
 We are looking for a talented Data Engineer to join our team and play a pivotal role in transforming our data ecosystem. The ideal candidate will have extensive experience in handling data from diverse business areas and managing data sources from a range of commercial and custom tools. They should be proficient in data engineering technologies, including Snowflake, Talend (or similar ETL tools), and bring expertise in Python, dbt, and Airflow. Familiarity with API integrations and experience with Tableau or other data visualization tools would be an advantage.In this role, you will also work directly with business stakeholders, running workshops to gather requirements and convert them into actionable data solutions. Experience working in an agile environment is essential.
 
 
 
 Responsibilities
 
 
 
 Design, develop, and maintain ETL pipelines to extract, transform, and load data from various sources into our data warehouse.
 
 
 Collaborate with Data Analytics Product Owner and business stakeholders to understand data requirements and offer recommendations to improve data collection, processing, and analysis.
 
 
 Build and maintain data models to enable efficient SQL-based reporting and analysis.
 
 
 Implement data workflows and transformations using dbt, and orchestrate data pipelines with Airflow.
 
 
 Develop and maintain data integrations via APIs where necessary.
 
 
 Monitor, troubleshoot, and optimize data pipeline performance to ensure data accuracy, reliability, and availability.
 
 
 Collaborate with data engineers, data scientists, and analysts to develop data-driven solutions that drive business outcomes.
 
 
 
 Requirements
 
 
 
 Bachelor&rsquo;s degree in Computer Science, Information Systems, or a related field.
 
 
 5+ years of experience in data engineering.
 
 
 Proficiency with data management tools like Snowflake, Talend, or similar ETL tools.
 
 
 Strong experience with Python, dbt, and Airflow.
 
 
 Solid understanding of SQL and database design.
 
 
 Knowledge of API integrations.
 
 
 Experience with Tableau or other data visualization tools (nice to have).
 
 
 Demonstrated ability to work independently and within a collaborative team environment.
 
 
 Strong analytical and problem-solving skills.
 
 
 Excellent communication and interpersonal skills.
 
 
 English fluency is required; proficiency in French is a plus.
 
 
 Here at Atos, diversity and inclusion are embedded in our DNA. Read more about our commitment to a fair work environment for all.
 
 
 Atos is a recognized leader in its industry across Environment, Social and Governance (ESG) criteria. Find out more on our CSR commitment.
 
 
 
 Choose your future. Choose Atos.
 
</div>

SENIOR DATA ENGINEER

TAQA Water

<div>
 
 About The Job Junior Data Engineer
 
 
 
 As a Junior Data Engineer, you will be responsible for supporting the design, development, and maintenance of our data infrastructure. You will work closely with key users to identify efficient data processing and analysis of large datasets.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Assist in the design and implementation of data pipelines.
 </li>
 <li>
 Develop and maintain PySpark notebooks for data processing and analysis.
 </li>
 <li>
 Support the integration and management of data from various sources, including dataflows and data lakes.
 </li>
 <li>
 Work with both structured and unstructured data to ensure comprehensive data coverage.
 </li>
 <li>
 Collaborate with team members to optimize data workflows and improve data quality.
 </li>
 <li>
 Monitor and troubleshoot data processing systems to ensure reliability and performance.
 </li>
 <li>
 Document data processes and workflows for future reference.
 </li>
 <li>
 Stay updated with the latest industry trends and technologies in data engineering.
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, Information Technology, or a related field from1 to 2 years' experience
 </li>
 <li>
 Proficiency in PySpark and Python programming.
 </li>
 <li>
 Knowledge of Microsoft Fabric components and their application in data engineering.
 </li>
 <li>
 Familiarity with SQL and database management systems, including data warehouses.
 </li>
 <li>
 Familiarity with data gateways for secure data transfer.
 </li>
 <li>
 Strong problem-solving skills and attention to detail.
 </li>
 <li>
 Excellent communication and teamwork abilities.
 </li>
 </ul>
</div>

Junior Data Engineer

Accenture

<div>
 
 Accenture Technology:
 
 
 
 Through unmatched industry experience, leading-edge technologies, and a global delivery network, Accenture helps organizations accelerate innovation and drive meaningful transformation. By combining data, intelligence, and advanced engineering capabilities, we enable our clients to build scalable, high‑quality data solutions that power business decisions and operational excellence. Visit us at www.accenture.com.
 
 
 
 The Work:
 
 
 
 As a
 
 Data Engineer
 
 , you will be responsible for designing, building, and maintaining robust data solutions that support data generation, collection, migration, and processing across enterprise systems. You will play a key role in developing scalable data pipelines, ensuring data quality, and implementing ETL processes to enable reliable data movement and analytics. This role requires strong technical expertise, ownership of team decisions, and close collaboration with multiple stakeholders to deliver impactful, cross‑team solutions.
 
 
 
 Responsibilities:
 
 
 
 
 <li>
 Design, develop, and maintain end-to-end data pipelines to support data ingestion, transformation, and deployment across systems.
 
 </li>
 <li>
 Implement and manage ETL processes to ensure efficient and reliable data migration and processing.
 
 </li>
 <li>
 Ensure high standards of data quality, consistency, and integrity through continuous monitoring and optimization.
 
 </li>
 <li>
 Act as a subject matter expert, providing technical guidance and contributing to key architectural and design decisions.
 
 </li>
 <li>
 Collaborate with cross-functional teams to identify data requirements and deliver effective, scalable data solutions.
 
 </li>
 <li>
 Lead and support the team to ensure effective execution, timely delivery, and alignment with business objectives.
 
 </li>
 <li>
 Develop innovative data solutions that improve operational efficiency and enable data-driven decision-making.
 
 </li>
 <li>
 Monitor, troubleshoot, and optimize data pipelines for performance, reliability, and scalability.
 
 </li>
 <li>
 Conduct regular data quality assessments and implement continuous improvements.
 
 </li>
 <li>
 Stay current with industry trends, emerging technologies, and best practices in data engineering.
 
 
 
 </li>
 
 Qualifications:
 
 
 
 
 <li>
 minimum 10 Years of experience.
 
 </li>
 <li>
 Master proficiency in Oracle Data Integrator (ODI) is required.
 
 </li>
 <li>
 Master proficiency in Data Migrations is highly recommended.
 
 </li>
 <li>
 Expert proficiency in Data Engineering, Data Pipelines, Data Processing, and Data Access is strongly preferred.
 
 </li>
 <li>
 Strong analytical, problem-solving, and collaboration skills, with the ability to work across multiple teams.
 
 
 
 </li>
</div>

Oracle Data Conversion Manager

Orange Business

<div>
 
 About Us
 
 
 
 Join us at Orange Business!
 
 
 We are a network and digital integrator that understands the entire value chain of the digital world, freeing our customers to focus on the strategic initiatives that shape their business.
 
 
 Every day, you will collaborate with a team dedicated to providing consistent, sustainable global solutions, no matter where our customers operate. With over 30,000 employees across Asia, the Americas, Africa, and Europe, we offer a dynamic environment to develop and perfect your skills in a field filled with exciting challenges and opportunities.
 
 
 
 About The Role
 
 
 
 <ul>
 <li>
 Translate reporting and analytics needs into scalable data models (star/snowflake), conformed dimensions, and data marts.
 </li>
 <li>
 Evolve the data warehouse architecture and schema design to support new use cases and performance goals.
 </li>
 <li>
 Build and maintain ETL/ELT pipelines in Python/SQL; orchestrate workflows and enforce data quality checks.
 </li>
 <li>
 Implement a semantic/metrics layer to support consistent KPIs and enable self-service analytics.
 </li>
 <li>
 Define and run data quality tests, anomaly detection, lineage, and documentation to improve trust in data.
 </li>
 <li>
 Integrate diverse data sources (operational systems, relational DBs, Elasticsearch, APIs, message queues).
 </li>
 <li>
 Optimize performance (partitioning, indexing, clustering, materialized views) for reporting and ad-hoc analysis.
 </li>
 <li>
 Maintain security, confidentiality, and integrity of data in line with relevant regulations; implement RBAC and data masking where appropriate.
 </li>
 <li>
 Partner with analysts to design dashboards and reporting assets; enable BI best practices and governance.
 </li>
 <li>
 Participate in the on-call rotation to ensure data pipelines and services remain reliable.
 
 
 </li>
 </ul>
 
 About You
 
 
 
 <ul>
 <li>
 Advanced SQL and data modeling skills (dimensional modeling, SCDs, normalization vs. denormalization).
 </li>
 <li>
 Strong Python for data processing (e.g., pandas, SQLAlchemy) and building ETL/ELT with automated testing.
 </li>
 <li>
 Experience with CI/CD for data (Git, code reviews, data tests using dbt/Great Expectations or similar).
 </li>
 <li>
 Experience with at least one of: PostgreSQL, MySQL/MariaDB, or Elasticsearch.
 </li>
 <li>
 Orchestration and transformation tools (e.g., Airflow/Prefect and/or dbt).
 </li>
 <li>
 Comfortable with Linux, Docker, and Git.
 </li>
 <li>
 BI/visualization experience (e.g., Power BI, Tableau, Looker, Superset, or Metabase) and dashboard design fundamentals.
 </li>
 <li>
 Writing tests gives you confidence in your code.
 </li>
 <li>
 Willingness to work in an on-call rotation.
 </li>
 <li>
 Full professional proficiency in English (written and verbal ) essential
 </li>
 <li>
 4+ years of relevant experience.
 
 
 </li>
 </ul>
 
 Plus:
 
 
 
 <ul>
 <li>
 Experience with message queues and streaming (RabbitMQ, Kafka).
 </li>
 <li>
 Exposure to modern cloud warehouses (Snowflake, BigQuery, Redshift) or columnar/time-series stores (ClickHouse, TimescaleDB).
 </li>
 <li>
 Experience with Kubernetes for deploying data workloads.
 </li>
 <li>
 Data catalog/lineage tooling (DataHub, OpenLineage, Amundsen) and metrics layer design (dbt metrics, LookML, semantic layers).
 </li>
 <li>
 Security telemetry analytics experience and governance for sensitive data (RBAC, masking, GDPR).
 
 
 </li>
 </ul>
 You bring a can-do attitude, tackle challenges head-on and challenge the status quo with new and innovative ideas.
 
 
 
 What We Offer
 
 
 
 <ul>
 <li>
 Global Opportunities: Work in multi-national teams with opportunity to collaborate with colleagues and customers from all over the world.
 </li>
 <li>
 Flexible Work Environment: Flexible working hours and possibility to combine work from office and home (hybrid ways of working).
 </li>
 <li>
 Professional Development: training programs and upskilling/re-skilling opportunities.
 </li>
 <li>
 Career Growth: Internal growth and mobility opportunities within Orange.
 </li>
 <li>
 Caring and Daring Culture: Health and well-being programs and benefits, diversity &amp; inclusion initiatives, CSR and employee connect events.
 </li>
 <li>
 Reward Programs: Employee Referral Program, Change Maker Awards.
 
 
 </li>
 </ul>
 
 Only Your Skills Matter
 
 
 
 Regardless of your age, gender identity, race, ethnic origin, religion/belief, sexual orientation, marital status, neurotype, disability, veteran status or appearance, we encourage diversity within our teams because it is a strength for the collective and a vector of innovation. Orange Group is a disabled-friendly company and equal opportunity employer: don't hesitate to tell us about your specific needs.
</div>

Analytics Engineer

<div>
 
 Overview:
 
 
 
 As an Oracle Data Conversion Lead at Accenture, you will play a pivotal role in designing, developing, and maintaining robust data solutions to support data generation, collection, and processing. Your responsibilities will include creating and optimizing data pipelines, ensuring data quality, and implementing ETL processes to facilitate seamless data migration and deployment across systems.
 
 
 You will act as a subject matter expert in Oracle Data Integrator (ODI) and Data Migrations, leveraging your expertise to guide and manage teams effectively. You will be responsible for driving team decisions, collaborating with multiple teams, and contributing to strategic initiatives while delivering innovative solutions to complex challenges.
 
 
 
 Key Responsibilities:
 
 
 
 
 <li>
 Lead the design and implementation of data solutions that enhance operational efficiency and scalability.
 
 </li>
 <li>
 Collaborate with cross-functional teams to identify data requirements and deliver effective solutions.
 
 </li>
 <li>
 Develop and optimize data pipelines to ensure performance, reliability, and scalability.
 
 </li>
 <li>
 Conduct regular assessments of data quality and implement improvements to maintain high standards.
 
 </li>
 <li>
 Stay abreast of industry trends, emerging technologies, and best practices in data engineering and Oracle Data Integration.
 
 </li>
 <li>
 Provide mentorship and guidance to team members, fostering a culture of collaboration and continuous improvement.
 
 </li>
 <li>
 Master proficiency in Oracle Data Integrator (ODI).
 
 </li>
 <li>
 Expert proficiency in Data Migrations, Data Engineering, Data Pipelines, and Data Processing.
 
 </li>
 <li>
 Strong problem-solving skills with the ability to provide solutions across multiple teams.
 
 </li>
 <li>
 Excellent communication and leadership skills to manage and collaborate effectively with diverse teams.
 
 
 
 </li>
</div>

Oracle Data Conversion Lead - Associate Manager

QNBeyond Plus

<div>
 
 About the Role
 
 
 We are looking for a motivated
 
 Data Engineer
 
 to join our
 
 Data Management and AI team
 
 and contribute to building scalable data platforms that power analytics and AI initiatives across our digital banking ecosystem.
 
 
 In this role, you will design and develop
 
 data pipelines, streaming infrastructure, and data warehouse solutions
 
 that enable reliable and high-performance data processing.
 
 
 This is an excellent opportunity for a
 
 junior data engineer
 
 looking to grow within a fast-paced fintech environment while working on modern data technologies.
 
 
 Team Overview
 
 
 The
 
 Data Management and AI team
 
 works across the entire technology stack within QNBeyond Plus.
 
 
 Our team collaborates with engineering, analytics, and product teams to manage, process, and analyze data that enables innovative digital banking experiences for our customers.
 
 
 Key Responsibilities
 
 
 Data Pipeline Development
 
 <ul>
 <li>
 Design and build scalable
 
 data pipelines for batch and real-time processing
 
 .
 </li>
 <li>
 Implement ETL/ELT workflows integrating data from
 
 APIs, databases, and event streams
 
 .
 </li>
 </ul>
 
 Streaming &amp; Event Processing
 
 <ul>
 <li>
 Develop and maintain
 
 streaming data pipelines using tools such as Kafka or similar platforms
 
 .
 </li>
 <li>
 Ensure
 
 low-latency, reliable, and fault-tolerant data delivery
 
 .
 </li>
 </ul>
 
 Data Transformation
 
 <ul>
 <li>
 Develop
 
 data enrichment and transformation workflows
 
 to convert raw data into business-ready datasets.
 </li>
 <li>
 Implement SQL transformations across layered architectures (
 
 raw &rarr; bronze &rarr; silver &rarr; gold
 
 ).
 </li>
 </ul>
 
 Data Warehouse Development
 
 <ul>
 <li>
 Design and maintain
 
 data warehouse models and schemas
 
 .
 </li>
 <li>
 Optimize query performance and storage design for analytical workloads.
 </li>
 </ul>
 
 Data Quality &amp; Reliability
 
 <ul>
 <li>
 Implement
 
 data validation and quality checks
 
 .
 </li>
 <li>
 Monitor data pipeline performance and ensure reliability.
 </li>
 </ul>
 
 Collaboration
 
 <ul>
 <li>
 Work closely with
 
 engineering, analytics, and product teams
 
 to deliver production-ready data solutions.
 </li>
 </ul>
 
 What We Are Looking For
 
 
 You must be:
 
 <ul>
 <li>
 
 Self-motivated
 
 , proactive, and results-driven
 </li>
 <li>
 A
 
 team player
 
 who enjoys collaboration and learning
 </li>
 <li>
 A
 
 problem solver
 
 who approaches challenges analytically
 </li>
 <li>
 
 Customer-oriented
 
 , committed to delivering high-quality data solutions
 </li>
 <li>
 
 Detail-oriented
 
 , especially regarding data quality and compliance
 </li>
 <li>
 Adaptable and eager to grow in a
 
 fast-paced fintech environment
 
 </li>
 </ul>
 
 Required Qualifications
 
 
 Education
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in
 
 Computer Science, Computer Engineering, or related field
 
 </li>
 </ul>
 
 Technical Knowledge
 
 <ul>
 <li>
 Understanding of
 
 computer systems and cloud environments
 
 </li>
 <li>
 Understanding of
 
 network protocols and system connectivity
 
 </li>
 </ul>
 
 Systems Skills
 
 <ul>
 <li>
 Experience using
 
 Linux command line tools
 
 </li>
 <li>
 Familiarity with
 
 SSH, SFTP, Bash, IP, ports, and networking concepts
 
 </li>
 </ul>
 
 Data Engineering Skills
 
 <ul>
 <li>
 Experience developing and optimizing
 
 ETL/ELT pipelines
 
 </li>
 <li>
 Experience with tools such as
 
 Informatica PowerCenter, Kafka, NiFi, or similar
 
 </li>
 </ul>
 
 Data &amp; Analytics
 
 <ul>
 <li>
 Strong
 
 SQL skills
 
 </li>
 <li>
 Understanding of
 
 data warehousing concepts and schema design
 
 </li>
 <li>
 Experience with
 
 data modeling and data lifecycle management
 
 </li>
 </ul>
 
 Programming
 
 <ul>
 <li>
 Programming experience using
 
 Python
 
 </li>
 <li>
 Scripting experience using
 
 Bash or similar tools
 
 </li>
 </ul>
 
 Experience
 
 <ul>
 <li>
 
 2+ years of experience working with ETL or data engineering workflows
 
 </li>
 <li>
 Experience with
 
 Informatica PowerCenter and MDM
 
 </li>
 <li>
 Experience developing and scaling
 
 data models within modern data warehouse architectures
 
 </li>
 <li>
 Experience implementing
 
 data enrichment and transformation logic
 
 </li>
 <li>
 Experience optimizing queries and storage design for analytics workloads
 </li>
 <li>
 At least
 
 1 year of experience in fintech, regulated environments, or PCI/DSS compliance
 
 is preferred
 </li>
 <li>
 Familiarity with
 
 performance monitoring tools
 
 </li>
 </ul>
</div>

Data Engineer

Interact Technology Solutions

<div>
 
 Job Summary
 
 
 
 The
 
 Senior AI Data Engineer
 
 is responsible for designing, building, and optimizing enterprise-scale data and AI infrastructure to support machine learning models, generative AI applications, and real-time analytics. The role drives the development of end-to-end data pipelines, from ingestion to production-ready AI data products, ensuring scalability, performance, and compliance across multi-cloud environments.
 
 
 
 Accountability &amp; Responsibilities
 
 
 
 <ul>
 <li>
 Design, build, and maintain scalable ETL/ELT data pipelines using modern data engineering tools (e.g., Apache Spark, dbt).
 </li>
 <li>
 Architect and implement Lakehouse data platforms (Delta Lake, Apache Iceberg, Apache Hudi) following Medallion architecture (Bronze/Silver/Gold).
 </li>
 <li>
 Develop real-time streaming pipelines using Apache Kafka, Apache Flink, and Spark Structured Streaming.
 </li>
 <li>
 Build and optimize AI/GenAI data pipelines for LLM training, fine-tuning, and inference (tokenization, dataset curation, prompt engineering datasets).
 </li>
 <li>
 Design and implement Retrieval-Augmented Generation (RAG) pipelines, including embedding workflows and vector database integration.
 </li>
 <li>
 Manage feature stores for real-time and batch machine learning use cases.
 </li>
 <li>
 Integrate data pipelines with AI/ML platforms (Databricks MLflow, Azure ML, AWS SageMaker, Vertex AI, OpenAI/Azure OpenAI).
 </li>
 <li>
 Implement data orchestration workflows using Apache Airflow or similar tools with CI/CD pipelines.
 </li>
 <li>
 Ensure data quality, governance, and security using frameworks such as Great Expectations and data catalog tools.
 </li>
 <li>
 Deploy and manage infrastructure using Infrastructure-as-Code tools (Terraform, Bicep, CDK).
 </li>
 <li>
 Collaborate with Data Scientists, ML Engineers, and Solution Architects to deliver production-ready AI solutions.
 </li>
 <li>
 Lead technical design decisions, mentor junior engineers, and contribute to data platform strategy.
 </li>
 <li>
 Maintain documentation, data contracts, and operational runbooks for all pipelines.
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 
 1
 
 &ndash; Required Experience
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Engineering, or related field.
 </li>
 <li>
 4&ndash;5 years of experience in data engineering, with strong exposure to AI/ML data infrastructure.
 </li>
 <li>
 Proven experience building scalable data pipelines and working with large-scale datasets.
 </li>
 <li>
 Hands-on experience with AI/ML platforms and modern data architectures.
 </li>
 <li>
 Experience in regulated industries (e.g., Banking, Telecom, Healthcare) is a plus.
 </li>
 <li>
 Strong problem-solving, analytical thinking, and communication skills.
 </li>
 <li>
 Experience working in cross-functional teams and agile environments.
 
 
 </li>
 </ul>
 
 2&ndash; Technical Skills
 
 
 
 <ul>
 <li>
 Strong SQL and advanced data modeling techniques
 </li>
 <li>
 Apache Spark (PySpark, Spark SQL, Streaming)
 </li>
 <li>
 Python (pandas, PySpark, data processing libraries)
 </li>
 <li>
 Data pipeline orchestration (Apache Airflow)
 </li>
 <li>
 CI/CD for data pipelines (GitHub Actions / Azure DevOps)
 </li>
 <li>
 Lakehouse architectures (Delta Lake / Iceberg / Hudi)
 </li>
 <li>
 Streaming technologies (Kafka, Flink)
 </li>
 <li>
 Cloud platforms (AWS / Azure / GCP)
 </li>
 <li>
 Vector databases (Pinecone, Weaviate, pgvector, OpenSearch)
 </li>
 <li>
 RAG pipeline design and LLM data processing
 </li>
 <li>
 Infrastructure-as-Code (Terraform / Bicep / CDK)
 </li>
 <li>
 Containers (Docker, Kubernetes)
 </li>
 <li>
 Data quality &amp; governance tools
 </li>
 </ul>
</div>

Senior AI Data Engineer

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