Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock – from around the world. In Data, we are responsible for delivering this data, news and analytics through innovative technology - quickly and accurately. We apply problem-solving skills to identify innovative workflow efficiencies, and we implement technology solutions to enhance our systems, products and processes - all while providing platinum customer support to our clients.

Our Team:

Our Listed Derivatives data team is seeking a Data Engineer to help us drive the data set forward. The team provides our users with complete and accurate, normalized security listings and reference data for tens of millions of securities across more than 100 Derivatives exchanges globally. Cross-functional collaboration, deep domain knowledge, thoughtful automation, and data management expertise are paramount for our ability to continuously deliver high quality data to our rapidly growing client base.

What’s the Role?

We are looking for a highly motivated individual with a passion for finance, data, and technology to increase value from our data product. In this role you will be responsible for developing strategies to optimize the value of Derivatives data for our clients and improve data operations. You will be a technical leader in the distributed team, solving problems with technical skills and devising solutions for data challenges. You will set strategy for quality enhancements of Derivatives reference data and work closely with various partners to integrate our solution into the wider architecture. You will join a distributed team of dedicated individuals and experts where your interests and passion will have a significant impact on the team’s success.

We trust you to:

• Build and maintain robust and scalable data pipelines to support the ingestion, transformation, and loading of vast amounts of data from various sources using Bloomberg tech stack components such as Bloomberg Data Services, Dataflow recipes and Data Technologies Pipelines or equivalent tech stack such as Amazon S3, Amazon Web Services Lambda, Kafka, Python Pandas
• Devise data quality strategies across various data workflows and oversee its governance
• Devise and implement data acquisition strategies for data fields from diverse and disparate, structured and unstructured sources
• Set up business rules and visualization to measure and ensure the accuracy, timeliness, and completeness of derivatives data using Bloomberg tech stack components such as business rule engines and QlikSense
• Design producer database structures optimized for our specific use cases
• Analyze internal processes to find opportunities for improvement and process engineer efficient and innovative workflows using programmatic machine learning approaches
• Use your deep understanding of listed derivatives markets and data, including trading and analytics workflows, to create comprehensive and transparent solutions that fits the use case of our internal and external clients
• Understand clients' and markets' needs on each derivative data field to extract and maintain it
• Collaborate with partners in creating data manipulation frameworks and establishing standard methodologies using Bloomberg's tech stack
• Apply your proven project management skills to ensure all technical projects are on track with right requirements
• Be responsive, resourceful, flexible and an excellent collaborator - Partner with our Product, Technology and Data Management Lab team to ensure consistent principles are leveraged, tools are fit for purpose, and results will be measurable
• Balance the best of technical and product knowledge to craft solutions for customers.

You'll need to have:

• 3-8 years of experience working with Python, SQL and/or NoSQL
• 3-8 years of experience working in a data engineering role
• Demonstrated experience in data management and experience in building ETL pipelines
• A bachelor of arts/bachelors of science degree or higher in Computer Science, Mathematics, or relevant data technology field, or  degree-equivalent qualifications
• Demonstrated continuous career growth within an organization
• Excellent written and verbal communication skills in English
• 4+ years experience of experience related to ingesting and normalizing exchange disseminated derivatives data
• Proficient in using Tech Stack such as Amazon S3, Lambda function, Kafka, Apache Airflow or Bloomberg Tech Stack in production environment
• Exceptional problem-solving skills, numerical proficiency and high attention to detail
• Ability to work independently as well as in a distributed team environment
• Ability to optimally communicate and present concepts and methodologies to diverse audiences

We'd Love to See:

• Data Management Association Certified Data Management Professional, Data Capability Assessment Model certification

If this sounds like you:

Apply if you think we're a good match. We'll get in touch to let you know what the next steps are, but in the meantime feel free to have a look at this: https://www.bloomberg.com/professional

Why Bloomberg?

We’re individuals with diverse backgrounds, talents, and experiences who take on big challenges and create even bigger impact through our work. We’re interested in what makes you, and how we can create opportunities for you to channel your unique, personal energy and grow to your fullest potential.

Learn more about our office and benefits:

Singapore | www.bloomberg.com/singapore

Bloomberg is an equal opportunity employer, and we value diversity at our company. We do not discriminate on the basis of age, ancestry, color, gender identity or expression, genetic predisposition or carrier status, marital status, national or ethnic origin, race, religion or belief, sex, sexual orientation, sexual and other reproductive health decisions, parental or caring status, physical or mental disability, pregnancy or parental leave, protected veteran status, status as a victim of domestic violence, or any other classification protected by applicable law.

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