At Bloomberg, our products are fueled by powerful data. Our Global Data department combines technology with product expertise to bring unequalled value to the world's information. This information comes from a variety of sources both structured and unstructured, including company filings, web scraped content and alternative data. We collect, analyze, process and publish the data which is the backbone of our iconic Bloomberg Terminal & data products that ultimately drive the financial markets.
Our department arose from a need to transform our business from largely manual processes to more scalable automated solutions. In order to continue the evolution of our data management operations, we are creating systems to manage the relentless flow of information. This not only saves us time and money but helps us deliver richer more actionable content. We're growing our team in order to scale our technology capabilities and expand the range of acquisition, extraction and enrichment techniques we use for automation.
What's in it for you?
Our department supports Global Data's software development and technical operations to shorten the development lifecycle - enabling us to provide data quickly and efficiently. We are leveraging advanced data processing and NLP as part of our pre-processing strategy. You will get exposure to our proprietary data pipelining, information extraction, and enrichment operations as part of our data processing paradigm.
Utilizing our modular tech stack, we are excited to expand both the data we process and the insights we provide. We are focused on combining and contributing to existing systems to build larger, more useful pipelines and services. We will work together to identify future projects and areas where we can have an impact. You'll be part of a team building data-driven systems supporting our internal businesses across Data Analytics, News, Research & Enterprise Data.
Who are you?
An autonomous, creative engineer who loves technology, is endlessly curious, aggressively inquisitive, and an advocate for best practices
You're a systems thinker who can see the big picture and understand how to break a larger problem down into smaller pieces that can be solved independently
Someone who is aligned to and excited by our departments core mission to build community, evolve and drive impact
You can build a network and find new opportunities for improvement
We'll trust you to:
Apply your python coding skills to help advance our goal of automating the influx of data and creating ETL solutions for our internal teams
Evangelize technical solutions and innovations among teams for further application
Identify strategic department level technical gaps in our ecosystem and advocate for new technologies and techniques
Leverage your development experience to build systems that are complete and robust enough to weave the data between automation and human expertise
Help build systems that utilize Machine Learning techniques to learn from human SME knowledge and actions
You'll need to have:
4+ years experience programming and scripting in Python within a production environment
2+ years experience with data modeling within SQL and NoSQL databases
2+ years experience working with restful APIs
Deep understanding of large-scale, distributed systems with an ability to understand large systems with minimal documentation
We'd love to see:
Systems thinkers - an interest or a natural preference to think about system architecture and how things work together
If this sounds like you apply!
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. - provided by Dice Associated topics: data analyst, data architect, data integration, data quality, data scientist, data warehouse, database administrator, hbase, mongo database administrator, teradata
* The salary listed in the header is an estimate based on salary data for similar jobs in the same area. Salary or compensation data found in the job description is accurate.