Intermediate / Senior Data Engineer

Senior AI Engineer

Data Engineer

Principal HANA Database Administrator

Senior Data Scientist 2

IAD / SAD / LAD GCP Data Engineer

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Data & Business Intelligence Analyst

Lead AI Engineer

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>

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>

Responsibilities: <ul><li>Dashboard Design and Development: Design, develop, and maintain interactive and user-friendly dashboards in PowerBI and Looker, focusing on clarity, performance, and data accuracy.&nbsp;</li><li>Data Lake Integration: Connect to and leverage data from various data lake sources on GCP, ensuring seamless data flow for visualization purposes.&nbsp;</li><li>Semantic Model Development: Develop and maintain robust semantic models in Looker (LookML) and PowerBI, ensuring data consistency, accuracy, and ease of use for reporting and analysis. This includes defining dimensions, measures, relationships, and calculations.&nbsp;</li><li>Machine Learning and GenAI Visualization: Create compelling visualizations to effectively communicate the results, performance, and insights derived from machine learning models and GenAI applications. This may involve visualizing model predictions, feature importance, natural language processing outputs, and other relevant data.&nbsp;</li><li>GCP Expertise: Utilize your strong understanding of GCP services to access, process, and prepare data for visualization. This may involve working with services such as BigQuery, Cloud Storage, and potentially data engineering pipelines built on GCP.&nbsp;</li><li>Collaboration: Partner closely with data scientists, data engineers, product managers, and business stakeholders to understand their data visualization needs and translate them into effective visual solutions.&nbsp;</li><li>Requirements Gathering: Elicit and document visualization requirements from various stakeholders, ensuring alignment with business objectives.&nbsp;</li><li>Performance Optimization: Optimize dashboard and visualization performance to ensure fast load times and efficient data rendering.&nbsp;</li><li>Data Quality Assurance: Ensure the accuracy, consistency, and reliability of data presented in visualizations.&nbsp;</li><li>Best Practices and Standards: Establish and enforce data visualization best practices and standards across the organization.&nbsp;</li><li>Training and Support: Provide guidance and support to other team members on data visualization tools and techniques.&nbsp;</li><li>Continuous Learning: Stay up to date with the latest trends and advancements in data visualization, PowerBI, Looker, GCP, and related technologies, particularly in the context of AI/ML.&nbsp;</li></ul>&nbsp;Requirements: <ul><li>Bachelor’s or master’s degree in computer science, Data Science, Statistics, Information Systems, or a related field.&nbsp;</li><li>Minimum of 3 years of experience as a Visualization Expert, Data Visualization Engineer, or a similar role.&nbsp;</li><li>Proven expertise in designing and developing interactive dashboards and reports using PowerBI and Looker.&nbsp;</li><li>Strong experience in connecting to and working with data lakes, preferably on the Google Cloud Platform (GCP).&nbsp;</li><li>Solid understanding and hands-on experience in developing semantic models, particularly with LookML in Looker and data modeling within PowerBI.&nbsp;</li><li>Preferable experience in creating visualizations specifically for machine learning and GenAI applications, understanding the nuances of presenting model outputs and insights.&nbsp;</li><li>Proficiency in SQL for data extraction and manipulation.&nbsp;</li><li>Familiarity with data warehousing concepts and data modeling techniques.&nbsp;</li><li>Experience with data preparation, ELT (preferred) / ETL processes.&nbsp;</li><li>Strong analytical and problem-solving skills with the ability to interpret complex data.&nbsp;</li><li>Excellent communication and presentation skills, with the ability to convey technical information to both technical and non-technical audiences.&nbsp;&nbsp;</li><li>Ability to work independently and collaboratively in a fast-paced environment.&nbsp;&nbsp;</li><li>Strong portfolio showcasing your data visualization skills and experience.&nbsp;</li></ul>

Responsibilities: <ul><li>Oversee the design, development, and deployment of AI models and intelligent agents.</li><li>Ensure engineering best practices in model development, testing, and deployment.</li><li>Collaborate with IT and data teams to ensure scalable and secure integration of AI solutions.</li><li>Work closely with business and technical stakeholders to understand requirements and deliver impactful AI solutions.</li><li>Communicate effectively with global teams, bridging technical and business perspectives.</li><li>Support change management and adoption of AI tools across business units.</li><li>Stay current with advancements in AI and apply them to solve business challenges.</li><li>Participate in efforts to improve enterprise adoption and proficiency in the use of AI tools.</li><li>Lead the design and development of solutions using Agentic AI&nbsp;</li></ul> Requirements: Basic Qualifications:<ul><li>10+ years of experience in software engineering, data science, AI, or a related field.</li><li>2+ years of experience in leadership role managing technical teams.</li><li>Proficiency in software development and statistical analysis.</li><li>Experience with Large Language Models and their applications.</li><li>Strong communication skills and the ability to present technical results to business stakeholders.</li><li>Experience with solution architecting and design.</li><li>Hands-on experience with cloud platforms such as Azure and/or AWS. </li><li>Proficiency in programming languages such as Python, R, or similar.</li><li>Bachelor’s or master’s in computer science, Mathematics, Statistics, or a related field.</li></ul> Preferred Qualifications:<ul><li>Experience working in a global enterprise or matrixed organization.</li><li>Familiarity with cloud platforms (Azure, AWS, GCP) and MLOps tools.</li><li>Experience with large language models and supporting workflows.</li><li>History of project leadership and development.</li><li>Experience in applications to sales, marketing, and market research&nbsp;</li></ul>

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16 gcp Jobs found

Intermediate / Senior Data Engineer

Senior AI Engineer

Data Engineer

Principal HANA Database Administrator

Senior Data Scientist 2

IAD / SAD / LAD GCP Data Engineer

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Data & Business Intelligence Analyst

Lead AI Engineer

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16 gcp Jobs found

Intermediate / Senior Data Engineer

Senior AI Engineer

Data Engineer

Principal HANA Database Administrator

Senior Data Scientist 2

IAD / SAD / LAD GCP Data Engineer

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Data & Business Intelligence Analyst

Lead AI Engineer

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WhiteCrow is on a mission to connect companies with the best talent across the globe.