Intermediate / Senior Data Engineer

IAD / SAD / LAD GCP Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Java Backend 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>

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>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>Collaborate with team, high availability, concurrent and fault tolerant software systems.</li><li>Significantly contribute to development of Aladdin’s global, multi-asset trading platform.</li><li>Work with product management and business users to define the roadmap for the product.</li><li>Design and develop innovative solutions to complex problems, identifying issues and roadblocks.</li><li>Apply validated quality software engineering practices through all phases of development.</li><li>Ensure resilience and stability through quality code reviews, unit, regression and user acceptance testing, dev ops and level two production support.</li><li>Be a leader with vision and a partner in brainstorming solutions for team productivity, efficiency, guiding and motivating others.</li><li>Drive a strong culture by bringing principles of inclusion and diversity to the team and setting the tone through specific recruiting, management actions and employee engagement.</li><li>For VP Level: In addition to above, a VP level candidate should be able to lead individual projects priorities, deadlines and deliverables using AGILE methodologies.</li></ul> Requirements: Qualifications: <ul><li>B.S. / M.S. degree in Computer Science, Engineering, or a related subject area</li><li>For VP Level: 8+ years of proven experience</li><li>For Associate Level: 4+ years of experience</li></ul> Skills and Experience: <ul><li>A proven foundation in core Java and related technologies, with OO skills and design patterns</li><li>Track record building high quality software with design-focused and test-driven approaches</li><li>Good hands-on object-oriented programming knowledge in Java, Spring, TypeScript, JavaScript, Microservices, Angular (2.0+), React.</li><li>Strong knowledge of Open-Source technology stack (Spring, Hibernate, Maven, JUnit, etc.).</li><li>Experience with relational database and/or NoSQL Database (e.g., Apache Cassandra)</li><li>Great analytical, problem-solving and communication skills</li><li>Some experience or a real interest in finance, investment processes, and/or an ability to translate business problems into technical solutions.</li><li>For VP Level: In addition to the above, a VP level candidate should have experience leading development teams, projects or being responsible for the design and technical quality of a significant application, system, or component. Ability to form positive relationships with partnering teams, sponsors, and user groups.</li></ul> Nice to have and opportunities to learn: <ul><li>Exposure to building microservices and APIs ideally with REST, Kafka or gRPC.</li><li>Experience working in an agile development team or on open-source development projects.</li><li>Experience with optimization, algorithms or related quantitative processes.</li><li>Exposure to high scale distributed technology like Kafka, Mongo, Ignite, Redis</li><li>Experience with Cloud platforms like Microsoft Azure, AWS, Google Cloud</li><li>Experience with DevOps and tools like Azure DevOps</li><li>Experience with AI-related projects/products or experience working in an AI research environment.</li><li>A degree, certifications or opensource track record that shows you have a mastery of software engineering principles.</li></ul>

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Intermediate / Senior Data Engineer

IAD / SAD / LAD GCP Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Java Backend Engineer

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Intermediate / Senior Data Engineer

IAD / SAD / LAD GCP Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Java Backend Engineer

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

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