Data Transformation Architect

As a Data Transformation Architect at Talworx, your primary responsibilities include designing complex data and cloud architectures, managing the machine learning lifecycle, and building APIs for production models. You'll work closely with Data Scientists to construct data pipelines and experiment with high-quality machine learning services that drive business impact. You'll also supervise the scaling of machine learning ecosystems and collaborate with various teams to enhance their business lines through innovative data solutions.

To qualify for the Data Transformation Architect position at Talworx, candidates should have at least 5 years of experience with big-data technologies such as Hadoop and Spark, along with strong programming skills in Python or PySpark. Additionally, candidates should possess expertise in application, data, and infrastructure architecture disciplines, as well as knowledge of MLOps and ModelOps. A solid understanding of financial markets and excellent communication skills are also essential for this role.

The Data Transformation Architect plays a pivotal role in helping clients achieve their objectives by architecting and delivering machine learning solutions that effectively process vast datasets. This position focuses on building scalable technology components that enhance data-driven decision-making for clients across financial markets. By collaborating closely with Data Scientists and product teams, the architect ensures that the developed models provide actionable insights, ultimately empowering clients to navigate their unique business challenges.

In the Data Transformation Architect role at Talworx, you will leverage a cutting-edge technology stack including tools and frameworks such as Spark, AWS, and Python. Familiarity with containerization technologies like Docker and orchestration with Kubernetes, along with skills in big-data ecosystems, will be key to successfully designing and implementing machine learning solutions that meet client needs.

Talworx fosters a highly inclusive work environment where Data Transformation Architects can thrive. You will have the freedom to bring your whole self to work, contributing to a culture of innovation and collaboration. The position offers the flexibility of hybrid work arrangements and the opportunity to engage with a passionate team dedicated to delivering excellence in market intelligence and technology solutions.

Certainly! In discussing your experience, focus on specific projects where you implemented Hadoop or Spark, detailing how you utilized these technologies to solve data-related challenges. Emphasize your role in data pipeline construction, as well as any optimizations made to improve performance or scalability.

Start by outlining your method for understanding project requirements, followed by your approach to designing a robust architecture that considers data ingestion, processing, and deployment. Highlight any frameworks or best practices you follow to ensure scalability and maintainability of machine learning models.

Discuss your experience with model validation, testing, and monitoring. Explain the importance of using version control, continuous integration/continuous deployment (CI/CD) practices, and automated testing to maintain high standards and ensure that the models perform effectively over time.

Illustrate your collaborative approach by sharing examples of cross-functional projects where you closely worked with Data Scientists and product owners. Highlight established communication practices, tools you utilize for collaboration, and how you ensure alignment on project goals and timelines.

Frame your response with the context of the challenge, the steps you took to resolve it, and the outcome. Focus on your problem-solving skills, technical abilities, and the impact your solution had on the project or the client’s needs.

Detail your approach to designing data pipelines, including data collection, cleansing, transformation, and integration processes. Discuss the technologies used and any performance improvements or optimizations you implemented in your past roles.

Talk about the resources you use to stay informed, such as industry publications, online courses, webinars, and networking with peers. Emphasize the importance of continuous learning and how it contributes to your effectiveness as a Data Transformation Architect.

Enumerate the programming languages and tools you are skilled in, particularly focusing on Python, PySpark, and any other relevant technologies like Docker, Kubernetes, or cloud services (e.g., AWS). Share experiences where you effectively used these languages/tools to build machine learning solutions.

Share your practical experience implementing MLOps practices, including how you streamlined machine learning workflows to enhance collaboration and efficiency. Highlight any tools or frameworks you have used and the improvements achieved through these practices.

Discuss your strategies for ensuring scalability, such as modular design principles, using containerization and orchestration, and applying best practices for code maintainability. Provide examples of how you have implemented these principles in past projects.