Total 2 job vacancies by Unison Consulting Pte Ltd

We are seeking a talented and experienced Senior Data Engineer (Quantexa)with expertise in Hadoop, Scala, Spark, Elastic, Open Shift Container Platform (OCP) and DevOps practices. Elasticsearch to join our team. As a Data Engineer, you will play a crucial role in designing, developing, and optimizing big data solutions using Apache Spark, Scala, and Elasticsearch. You will collaborate with cross-functional teams to build scalable and efficient data processing pipelines and search applications. Knowledge and experience in the Compliance / AML domain will be a plus. Working experience with Quantexa tool is a must.

Responsibilities:

·        Implement data transformation, aggregation, and enrichment processes to support various data analytics and machine learning initiatives

·        Collaborate with cross-functional teams to understand data requirements and translate them into effective data engineering solutions

·        Design, develop, and implement Spark Scala applications and data processing pipelines to process large volumes of structured and unstructured data

·        Integrate Elasticsearch with Spark to enable efficient indexing, querying, and retrieval of data

·        Optimize and tune Spark jobs for performance and scalability, ensuring efficient data processing and indexing in Elasticsearch

·        Implement data transformations, aggregations, and computations using Spark RDDs, DataFrames, and Datasets, and integrate them with Elasticsearch

·        Develop and maintain scalable and fault-tolerant Spark applications, adhering to industry best practices and coding standards

·        Troubleshoot and resolve issues related to data processing, performance, and data quality in the Spark-Elasticsearch integration

·        Monitor and analyze job performance metrics, identify bottlenecks, and propose optimizations in both Spark and Elasticsearch components

·        Ensure data quality and integrity throughout the data processing lifecycle

·        Design and deploy data engineering solutions on OpenShift Container Platform (OCP) using containerization and orchestration techniques

·        Optimize data engineering workflows for containerized deployment and efficient resource utilization

·        Collaborate with DevOps teams to streamline deployment processes, implement CI/CD pipelines, and ensure platform stability

·        Implement data governance practices, data lineage, and metadata management to ensure data accuracy, traceability, and compliance

·        Monitor and optimize data pipeline performance, troubleshoot issues, and implement necessary enhancements

·        Implement monitoring and logging mechanisms to ensure the health, availability, and performance of the data infrastructure

·        Document data engineering processes, workflows, and infrastructure configurations for knowledge sharing and reference

1. More than 5 years of experience as a Data Engineer
2. · Bachelor's or Master's degree in Computer Science, Software Engineering, or a related discipline
3. · Possession of Quantexa certification as a Data Engineer or Data Architect, with proficiency in the tool
4. · Demonstrated experience as a Data Engineer, utilizing Hadoop, Spark, and data processing technologies in large-scale environments
5. · Expertise in the Scala programming language and familiarity with functional programming principles
6. · Prior experience with the Quantexa tool is highly desirable
7. · Comprehensive understanding of Apache Spark architecture, including RDDs, DataFrames, and Spark SQL
8. · Advanced proficiency in designing and developing data infrastructure utilizing Hadoop, Spark, and associated tools (HDFS, Hive, Pig, etc.)
9. · Experience with containerization platforms such as OpenShift Container Platform (OCP) and container orchestration via Kubernetes
10. · Proficiency in programming languages commonly employed in data engineering, including Spark, Python, Scala, or Java
11. · Knowledge of DevOps methodologies, CI/CD pipelines, and infrastructure automation tools (e.g., Docker, Jenkins, Ansible, BitBucket)
12. · Experience with Graphana, Prometheus, and Splunk will be considered an added advantage
13. · Background in integrating and utilizing Elasticsearch for data indexing and search applications
14. · Solid understanding of Elasticsearch data modeling, indexing strategies, and query optimization techniques
15. · Experience with distributed computing, parallel processing, and handling large datasets
16. · Proficient in performance tuning and optimization methods for Spark applications and Elasticsearch queries
17. · Strong problem-solving and analytical capabilities with the capacity to debug and resolve intricate issues
18. · Familiarity with version control systems (e.g., Git) and collaborative development environments

We seek individuals with highly developed conceptual, strategic, and analytical skills, capable of striking a balance between visionary thinking and practical solutions. The ability to comprehend, inspire, and mobilize others is crucial. A business-oriented mindset coupled with effective storytelling will drive your success. We are looking for self-starters ready to take on responsibilities with enthusiasm.

As an ML Engineer, your pivotal role involves operationalizing ML Models developed by Bank data scientists. You will serve as the focal point for ML model refactoring, optimization, containerization, deployment, and quality monitoring. Your main responsibilities will include:
Conduct reviews for compliance of the ML models in accordance with overall platform governance principles such as versioning, data / model lineage, code best practices and provide feedback to data scientists for potential improvements

Develop pipelines for continuous operation, feedback and monitoring of ML models leveraging best practices from the CI/CD vertical within the MLOps domain. This can include monitoring for data drift, triggering model retraining and setting up rollbacks.

Optimize AI development environments (development, testing, production) for usability, reliability and performance.

Have a strong relationship with the infrastructure and application development team in order to understand the best method of integrating the ML model into enterprise applications (e.g., transforming resulting models into APIs).

Work with data engineers to ensure data storage (data warehouses or data lakes) and data pipelines feeding these repositories and the ML feature or data stores are working as intended.

Evaluate open-source and AI/ML platforms and tools for feasibility of usage and integration from an infrastructure perspective. This also involves staying updated about the newest developments, patches and upgrades to the ML platforms in use by the data science teams.

• Proficiency in Python used both for ML and automation tasks
• Good knowledge of Bash and Unix/Linux command-line toolkit is a must-have.
• Hands on experience building CI/CD pipelines orchestration by Jenkins, GitLab CI, GitHub Actions or similar tools is a must-have.
• Knowledge of OpenShift / Kubernetes is a must-have.
• Good understanding of ML libraries such as Panda, NumPy, H2O, or TensorFlow.
• Knowledge in the operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g., Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, Dataiku, H2O, or DKube).
• Knowledge of Distributed Data Processing framework, such as Spark, or Dask.
• Knowledge of Workflow Orchestrator, such as Airflow or Ctrl-M.
• Knowledge of Logging and Monitoring tools, such as Splunk and Geneos.
• Experience in defining the processes, standards, frameworks, prototypes and toolsets in support of AI and ML development, monitoring, testing and operationalization.
• Experience in ML operationalization and orchestration (MLOps) tools, techniques and platforms. This includes scaling delivery of models, managing and governing ML Models, and managing and scaling AI platforms.