Software Engineer

Intermediate / Senior Data Engineer

Senior AI Engineer

Data Engineer

Principal HANA Database Administrator

Senior Data Scientist 2

IAD / SAD / LAD GCP Data Engineer

Senior Business Intelligence Analyst

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Role Description: <ul><li>SOFTWARE DEVELOPMENT: Partners to design and develop high quality software solutions by writing clean, maintainable and efficient codes.</li><li>AUTOMATION: Applies internal software deployment platform, methodologies and tools to automate the deployment process, ensuring smooth and reliable releases.</li><li>COLLABORATION: Partners with cross functional team of product managers, designers and other engineers to gather moderately complex requirements and deliver solutions that meet business needs.</li></ul> Responsibilities: <ul><li>SOFTWARE DEVELOPMENT: Partners to design and develop high quality software solutions by writing clean, maintainable and efficient codes.</li><li>AUTOMATION: Applies internal software deployment platform, methodologies and tools to automate the deployment process, ensuring smooth and reliable releases.</li><li>COLLABORATION: Partners with cross functional team of product managers, designers and other engineers to gather moderately complex requirements and deliver solutions that meet business needs.</li><li>TESTING &amp; DEBUGGING: Writes and maintains moderately complex unit tests and integration tests, and performs debugging to maintain the quality and performance of the software.</li><li>CONTINUOUS IMPROVEMENT: Makes suggestions for improving the software development and deployment processes, and implements the approved standards to improve efficiency and reliability.</li><li>DOCUMENTATION: Drafts and maintains comprehensive documentation for moderately complex software applications, deployment processes and system configurations.</li><li>TECHNICAL SUPPORT: Provides technical support and troubleshooting for moderately complex issues with deployed applications to ensure minimal downtime and fast resolution.</li></ul> Requirements: <ul><li>Software Engineer: Minimum requirement of 3 years of relevant work experience.&nbsp;</li><li>React/Typescript/UI</li><li>Java/Spring Boot/Python/APIs</li><li>PostgreSQL</li><li>CI/CD pipelines</li><li>AWS/Azure Cloud Services</li></ul>

Responsibilities: <ul><li>Designing, implementing, and optimising scalable data pipelines using Python, PySpark, and Databricks.</li><li>Driving the delivery of customer-facing data products through APIs, Databricks-based sharing, and event-driven mechanisms (e.g., Kafka or similar).</li><li>Taking ownership of end-to-end features — from scoping and development to deployment and monitoring.</li><li>Leading and participating in technical design discussions, contributing to architectural improvements and long-term data strategy.</li><li>Mentoring and coaching junior engineers actively while supporting their technical and professional development.</li><li>Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions.</li><li>Conducting peer code reviews, upholding code quality standards, and championing continuous improvement practices.</li><li>Contributing to the team’s agile delivery process and provide input during planning and retrospectives.</li></ul> Requirements: <ul><li>Have extensive experience in data engineering or backend software engineering with a data focus.</li><li>Be proficient in Python, PySpark, and working within Databricks environments.</li><li>Have hands-on experience designing and delivering data products via REST APIs, event-driven systems, or data sharing platforms like Databricks Delta Sharing.</li><li>Have a solid understanding of distributed data processing, ETL/ELT workflows, and data lake/lakehouse architectures.</li><li>Have experience with cloud platforms (e.g., AWS, Azure, GCP) and modern data tooling.</li><li>Have a proven track record of mentoring or coaching other engineers.</li><li>Have effective communication and collaboration skills, including the ability to explain complex concepts to non-technical stakeholders.</li><li>Be familiar with agile development practices</li></ul>

Role Description: <ul><li>We are building a new data engineering team to support a strategic move from legacy data estates to a modern data platform built on Databricks.</li><li>This role plays a key part in re-engineering existing systems, improving scalability, and enabling future innovation.</li></ul> Responsibilities: <ul><li>Transition legacy data systems to Databricks.</li><li>Build and maintain robust, scalable data pipelines.</li><li>Work closely with subject matter experts, product teams, and stakeholders to understand business logic.</li><li>Apply best practices in data engineering and programming.</li><li>Mentor junior engineers and contribute to knowledge sharing.</li><li>Participate in code reviews and technical discussions.</li></ul> Requirements: <ul><li>Hands-on experience in software or data engineering roles.</li><li>Strong proficiency in Python and working knowledge of PySpark.</li><li>Experience or strong interest in Databricks.</li><li>Solid understanding of SQL and structured data.</li><li>Experience with cloud platforms (AWS preferred; Azure/GCP acceptable).</li><li>Familiarity with REST APIs and event-driven architectures.</li><li>Experience using Git or similar version control systems.</li><li>Strong problem-solving skills and collaborative mindset.</li></ul> Preferred Experience <ul><li>Experience working on data platform modernization or migration projects.</li><li>Ability to refactor and improve legacy codebases.</li><li>Exposure to AI/ML data pipelines is an advantage.</li></ul> Why Join Us <ul><li>Opportunity to work on a strategic, high-impact data platform transformation.</li><li>Exposure to cutting-edge technologies including Databricks and cloud platforms.</li><li>Collaborative, inclusive team culture with strong mentorship.</li><li>Clear opportunities for career growth and skill development.</li></ul>

Role Overview: <ul><li>We are building a new data engineering team to support a strategic move from legacy data estates to a modern data platform built on Databricks.&nbsp;</li><li>This role plays a key part in re-engineering existing systems, improving scalability, and enabling future innovation.</li></ul> Responsibilities: <ul><li>Lead the transition of legacy data systems to Databricks, ensuring reliability and performance.</li><li>Design, build, and maintain robust, scalable data pipelines to support analytical and operational use cases.</li><li>Collaborate closely with subject matter experts, product teams, and business stakeholders to understand and implement business logic accurately.</li><li>Apply data engineering and programming best practices to ensure high-quality, maintainable, and secure solutions.</li><li>Mentor junior engineers, fostering technical growth and promoting knowledge sharing across the team.</li><li>Participate in code reviews, architectural discussions, and technical decision-making to uphold engineering standards.</li></ul> Requirements:&nbsp; Required Skills &amp; Experience: <ul><li>Hands-on experience in software or data engineering roles.</li><li>Strong proficiency in Python and working knowledge of PySpark.</li><li>Experience or strong interest in Databricks.</li><li>Solid understanding of SQL and structured data.</li><li>Experience with cloud platforms (AWS preferred; Azure/GCP acceptable).</li><li>Familiarity with REST APIs and event-driven architectures.</li><li>Experience using Git or similar version control systems.</li><li>Strong problem-solving skills and collaborative mindset.</li></ul> Preferred Experience: <ul><li>Experience working on data platform modernization or migration projects.</li><li>Ability to refactor and improve legacy codebases.</li><li>Exposure to AI/ML data pipelines is an advantage.</li></ul>

Role Description: <ul><li>The Principal HANA Database Administrator will serve as the organization’s top technical authority for SAP HANA database environments.</li><li>This role is responsible for designing, implementing, and optimizing large-scale HANA systems to ensure high availability, security, and performance.</li><li>The individual will lead strategic initiatives, mentor team members, and collaborate with cross-functional teams to support mission-critical enterprise applications.</li></ul> Responsibilities: Architecture &amp; Strategy <ul><li>Define enterprise-level SAP HANA architecture and standards.</li><li>Develop strategies for scalability, high availability, disaster recovery, and security compliance.</li></ul> Database Administration <ul><li>Install, configure, and upgrade SAP HANA systems.</li><li>Perform advanced performance tuning, query optimization, and memory management.</li><li>Monitor system health and proactively resolve issues.</li></ul> Data Management <ul><li>Manage data provisioning, replication, and integration with SAP and non-SAP systems.</li><li>Ensure compliance with data governance and security standards.</li></ul> Leadership <ul><li>Act as a subject matter expert for SAP HANA across the organization.</li><li>Mentor junior DBAs and provide technical guidance to project teams.</li><li>Drive automation and best practices for database operations.</li></ul> Innovation <ul><li>Evaluate emerging technologies and recommend enhancements.</li><li>Lead initiatives to improve efficiency and reduce operational risk.</li></ul> Requirements: Education: <ul><li>Bachelor’s or Master’s degree in Computer Science, Information Systems, or related field.</li></ul> Experience: <ul><li>10+ years of database administration experience, with at least 5 years in SAP HANA.</li><li>Proven track record managing large-scale, mission-critical HANA environments.</li></ul> Technical Skills: <ul><li>Expertise in SAP HANA architecture, sizing, and performance tuning.</li><li>Strong knowledge of Linux OS, virtualization, and cloud platforms (AWS, Azure, GCP).</li><li>Familiarity with SAP Basis and integration with S/4HANA, BW/4HANA.</li></ul> Certifications: <ul><li>SAP Certified Technology Specialist – SAP HANA.</li><li>Additional certifications in cloud or database technologies preferred.</li></ul>

Responsibilities: <ul><li>Lead the design and development of LLM-powered solutions for clinical summarization, intelligent chat, and document understanding to improve patient care outcomes.</li><li>Architect and implement end-to-end machine learning pipelines, including Retrieval-Augmented Generation (RAG) systems using vector databases such as Pinecone and Weaviate.</li><li>Fine-tune and optimize state-of-the-art language models (e.g., GPT, LLaMA, Mistral) and apply advanced prompt engineering to address specialty-specific healthcare use cases.</li><li>Own project delivery end to end, collaborating with stakeholders to ensure AI solutions are ethical, scalable, reliable, and well-governed.</li><li>Mentor and support junior data scientists, fostering a culture of experimentation, learning, and innovation.</li></ul> Requirements: <ul><li>Experience: 8–12 years in Data Science/ML, with at least 3+ years specifically focused on LLMs and Generative AI.&nbsp;</li><li>Technical Mastery: Deep hands-on expertise in Python and frameworks like PyTorch, Hugging Face, LangChain, or LlamaIndex.&nbsp;</li><li>Cloud Fluency: Proven ability to deploy models at scale using AWS SageMaker, Azure ML, or GCP Vertex AI.&nbsp;</li><li>MLOps Mindset: Experience with MLflow, Airflow, Docker, and Kubernetes to ensure our "bold innovations" are also stable and reliable.&nbsp;</li><li>Academic Foundation: A strong grasp of statistical analysis and evaluation techniques to mitigate hallucinations and ensure clinical accuracy.&nbsp;</li></ul>

Responsibilities: Architecture &amp; Design:&nbsp; <ul><li>Design end-to-end data architectures that integrate real-time streaming (Kafka, GCP Pub/sub) and batch processing (Big Query OR Databricks).</li></ul> Pipeline Development:&nbsp; <ul><li>Build and maintain robust ETL/ELT pipelines flowing into BigQuery/Databricks, ensuring data is clean, modeled, and ready for consumption.</li></ul> Custom Integration:&nbsp; <ul><li>Develop custom APIs and microservices using Java/Python/C# to facilitate data movement between niche systems and the data lakehouse.</li></ul> Infrastructure Management:&nbsp; <ul><li>Deploy and manage data workloads on Google Kubernetes Engine (GKE), ensuring high availability and cost-efficiency.</li></ul> Technical Leadership:&nbsp; <ul><li>Mentor junior engineers, conduct code reviews, and establish DataOps best practices (CI/CD, automated testing, and monitoring).</li></ul> Data Reliability:&nbsp; <ul><li>Implement data quality frameworks to ensure accuracy and lineage across the hybrid environment.</li></ul> Requirements: <ul><li>Experience with Terraform &amp; Azure Dev Ops (ADO).</li><li>Knowledge of workflow orchestrators like Apache Airflow (Cloud Composer).</li><li>Experience implementing Data Governance (metadata management, data cataloging).</li><li>Data Processing &amp; Storage</li><li>BigQuery: Expert-level SQL and experience with partition/cluster optimization.</li><li>Databricks: Deep knowledge of the Lakehouse architecture, Delta Lake, and Spark (PySpark/Scala) Software Engineering &amp; APIs</li><li>Languages: Proficiency in Java/Python/C# for backend development and custom data connectors.</li><li>API Design: Proven ability to build and consume RESTful APIs for data ingestion Streaming &amp; Infrastructure</li><li>Kafka/GCP Pub-Sub: Experience building event-driven architectures and managing producers/consumers.</li><li>GKE: Proficiency in containerization (Docker) and orchestrating data workloads on Kubernetes.</li></ul>

Role Description: <ul><li>The Continuous Improvement EMIA team reports to Operations Automotive EMIA.</li><li>By providing new generation digital solutions to our Operational teams across EMIA, our team creates full transparency and speed in the ongoing digital &amp; lean transformation(s), which will enable all involved functions to leverage data to develop business insights and roadmap updates based upon clearly arranged and understandable data. Due to the extended scope, focusing also on the connecting processes from Supplier(s) to Manufacturing ending at the Customer(s) (internal and external) we will function as ‘the’ differentiator for our Company.</li><li>The Data Scientist Automotive EMIA will report to the Smart Factory Leader Automotive EMIA.&nbsp;</li></ul> Responsibilities:&nbsp; <ul><li>Lead projects and work together with our Automotive EMIA Operations plants, internal (IT, , Corporate Technology, CoE, SC, ..) and external partners in the area of Smart Factory (Applications, ML, AI, DL), aligned with Operations Automotive EMIA Strategy.</li><li>Lead Smart Factory improvement projects (Enhancements) of existing applications together with internal / external supplier and customers (project management) until the level of RTD (Ready To Deploy)</li><li>·Use a combined knowledge of computer science and applications, modelling, statistics, analytics and maths to solve problems.</li><li>Identify and interpret data sources, manage large amounts of data, merge data sources together, ensure consistency of datasets, create visualizations to aid in understanding data.</li><li>Develop, Validate and Deploy (including Training and Coaching of the users) until the level of RTD (Ready To Deploy), Digital Factory Applications, ML models, contributing to waste elimination and efficiency improvements within all Operations functions (Manufacturing, Quality, SC, LOG, ….), this together with our internal and external partners.</li></ul>&nbsp;Requirements: Desired candidate &amp; Qualification <ul><li>Good understanding of a technology driven manufacturing company.</li><li>Knowledge of the core manufacturing technologies: Stamping, Plating, Assembly, Molding.</li><li>Experienced with data visualization tools like PowerBi, Tableau, ThingWorx,..</li><li>Good applied statistics skills, such as distributions, statistical testing, regression, etc.</li><li>Development experience in programming languages R, Python.</li><li>Knowledge, experience in JAVA scripting</li><li>Excellent pattern recognition and predictive modelling skills.</li><li>Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, neural networks and deep learning etc.</li><li>Experience in technologies like, Jupyter Notebooks, Numpy, Pandas, Seaborn, Scikit-learn, PyTorch and TensorFlow.</li><li>Exposure to recent developments in Deep Learning, Gen AI, Agentic AI domain.</li><li>Database skills, on-premises and cloud based.</li><li>Experience in AWS is an advantage, functional knowledge of AWS platforms such as Sagemaker, S3, and RedShift.</li><li>Excellent presentation skills in both spoken en written English.</li><li>&nbsp;Excellent and strong communicator</li><li>The ability to translate, explain and bring, complex data science topics to an easy understandable level, speeding up acceptance level in general.</li><li>Bachelor or master’s in data science</li><li>Team player with a solution focused problem-solving mentality.</li><li>Change agent with high Execution level, including training and coaching talents.</li><li>Passioned about the combination of Technology, Data, Analytics and Statistics with link to Quality, Product and Process Engineering</li><li>Inspired by and searching actively for new technologies and methods.</li></ul>

Responsibilities: <ul><li>Ability to troubleshoot problems in a live environment and provide real time help with critical issues.</li><li>Write code that is easily understood and maintainable by other team members.</li><li>Keep up to date with developments in technologies we are using.</li><li>Ability to communicate and work effectively, supporting various business departments.</li><li>Ability to work in fast-paced interdisciplinary environment.</li></ul> Requirements <ul><li>BA/BS in Computer Science or equivalent practical experience.&nbsp;</li><li>At least 3+ years of post-university experience as a full-stack engineer.</li><li>Solid knowledge of programming fundamentals—algorithms, data structures, design patterns, and paradigms.</li><li>Strong knowledge of Python.</li><li>Solid knowledge SQL and relational databases.</li><li>Knowledge of Extract-Transform-Load (ETL) and big data analytics tools.</li><li>Experience working in a cloud environment (AWS, Azure, GCP).</li><li>Familiarity with popular Python data analytics frameworks.</li><li>Familiarity with Snowflake or other large analytics engines.</li><li>Experience with data warehousing and working in environments with large scale, complex analytics requirements.</li><li>Familiarity with DAG-based job scheduling tools.</li><li>Cloud-native container orchestration platforms and tools.</li><li>Experience working in the Financial Services industry.</li></ul>

Responsibilities: <ul><li>Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.&nbsp;</li><li>Build and scale ML pipelines using Vertex AI Pipelines, Kubeflow, Airflow, and manage infra-as-code with Terraform/Helm.&nbsp;</li><li>Implement automated retraining, drift detection, and re-deployment of ML models.&nbsp;</li><li>Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.&nbsp;</li><li>Implement model monitoring, observability, and alerting across accuracy, latency, and cost.&nbsp;</li><li>Integrate and manage feature stores, knowledge graphs, and vector databases for advanced ML/RAG use cases.&nbsp;</li><li>Ensure pipelines are secure, compliant, and cost-optimized.&nbsp;&nbsp;</li><li>Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance .&nbsp;</li><li>Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.&nbsp;</li><li>Stay current with the latest ML research and apply new tools pragmatically to production systems.&nbsp;</li><li>Collaborate with product managers, DS, and engineers to translate business problems into reliable ML systems.&nbsp;</li></ul> Requirements: <ul><li>Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).&nbsp;</li><li>5+ years of experience in machine learning engineering, MLOps, or large-scale AI/DS systems.&nbsp;</li><li>Strong foundations in data structures, algorithms, and distributed systems.&nbsp;</li><li>Proficient in Python (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.&nbsp;</li><li>Hands-on experience building and deploying ML models at scale in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).&nbsp;</li><li>Experience with containerization (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).&nbsp;</li><li>Familiarity with CI/CD pipelines, infrastructure-as-code (Terraform/Helm), and configuration management.&nbsp;</li><li>Experience with big data and streaming technologies (Spark, Flink, Kafka, Hive, Hadoop).&nbsp;</li><li>Practical exposure to model observability tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)&nbsp;</li><li>Strong understanding of statistical methods, ML algorithms, and deep learning architectures.&nbsp;</li></ul>

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18 azure deveops Jobs found

Software Engineer

Intermediate / Senior Data Engineer

Senior AI Engineer

Data Engineer

Principal HANA Database Administrator

Senior Data Scientist 2

IAD / SAD / LAD GCP Data Engineer

Senior Business Intelligence Analyst

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

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18 azure deveops Jobs found

Software Engineer

Intermediate / Senior Data Engineer

Senior AI Engineer

Data Engineer

Principal HANA Database Administrator

Senior Data Scientist 2

IAD / SAD / LAD GCP Data Engineer

Senior Business Intelligence Analyst

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

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