7 Must-Have Big Data Skills You Need to Get a Great Job in Fintech

Nearly $42 billion has already been invested in fintech in the first half of 2018, surpassing last year’s total invested in just six months, according to FinTech Global.

Financial technology is a wildly popular and rapidly growing segment, so it only stands to reason that more job-seekers might be considering careers in fintech. While not all employers and not all jobs are the same, there are several particular sets of Big Data skills that will help power any job search in fintech. Here are the seven most crucial Big Data skills that can pave the way for a future-proof career.

Programming

Powering the infrastructure and architecture of all financial technology, programming skills are a must for those who want to work in hands-on roles. The most popular languages continue to be Java and JavaScript, though Python, which is easier to learn and use than most other languages, is gaining ground.

Other languages to consider include C++, C# and SQL, which all will make a candidate more attractive to employers and could even help you command a higher salary. Even for those not in hands-on technical roles, a general understanding of programming, particularly with Python and other Big Data-heavy tools like Hadoop and MapReduce, is crucial.

Database administration

Touching on topics from data storage and compression to security and encryption, fintech professionals must have a solid foundation in database administration. Working in the fintech industry means dealing with enormous amounts of incredibly sensitive information and navigating both professional best practices and legal frameworks governing the collection, storage and usage of that data.

Those seeking specialized jobs in database administration for fintech providers should have hands-on experience in technologies such as MySQL, Cassandra, Kafka and HBase.

Predictive analytics

Providing customers with a personalized experience, suggesting new products and reducing fraud and theft — those are just a few of the ways fintech promises to elevate predictive analysis, which is something the finance sector has always done. But Big Data makes for more accurate and quicker work.

Depending on the approach employers take to predictive analytics, they could be looking for candidates with a strong statistical modeling background or a heavy machine-learning background. Most roles will require knowledge of both as well as hands-on experience in SQL, data warehousing and mathematics.

Machine learning, AI & automation

Much of the promise of fintech lies in increasing efficiency and good decision-making. Thirty years ago, a loan officer likely made a personal decision about whether to extend credit to someone, and while a human expert is still needed today, through machine learning and automation, fintech companies can equip institutions with data-backed intelligence to help inform decision-making. AI and related technologies can help financial institutions improve everything from fraud detection and claims management to customer support to wealth management.

Job-seekers should have experience in areas such as Natural Language Processing, deep-learning algorithms, such as regression, classification and cluster analysis, and social media (SoMe) data collection and analysis.

Data mining

None of the Big Data technologies we’re talking about would be possible without getting your hands on the data in the first place. Particularly given increased governmental and consumer attention being placed on the collection and storage of personal data, such as the recently enacted GDPR rules in the EU, data mining experts will be highly sought-after.

Experience in statistics, unstructured data and algorithms are crucial, as is hands-on experience in Python and R languages.

Risk modeling

In a country still reeling from the subprime mortgage crisis, risk modeling is top of mind for many in the financial sector. Nobody can see the future, but those with aptitude in risk modeling can help simulate what might happen in any given financial event or decision. As with many of the areas on this list, Big Data both complicates (because of the sheer amount of information) and simplifies this challenge (because decisions are likely to be better informed).

Specialized skills needed in this area will depend on the employer, but many require hands-on experience in languages such as SAS, SQL and R and database management tools including Essbase, Excel and Teradata.

Decentralized ledger technology

Decentralized ledger technology, such as blockchain, represents the true frontier of financial technology. From current uses like smart contracts and cryptocurrency to possible future uses like interbank settlement and risk reduction, DLT has the potential to completely remake the financial landscape.

Experience programming in Python, C#, Go, Node.js, JavaScript and other languages will likely be required for hands-on roles in applying this technology, and skill sets that include UI/UX design also will be helpful.

The Future Is Fintech
We already know fintech is an attractive segment for investors and an exciting one for job-seekers. But, according to PricewaterhouseCoopers’ Global FinTech Report 2017, the sky may well be the limit: 82 percent of financial services companies are likely to increase fintech initiatives in the next few years, and 77 percent predict they’ll adopt blockchain technology by 2020.

With Big Data at the heart of fintech, touching everything from programming to design to implementation to governance and regulation, a bright career in fintech will almost certainly need to start with a strong Big Data foundation.

Jennifer L. Gaskin: Jennifer L. Gaskin is an Indiana-based writer & contributing Jobs Editor for BusinessStudent.com. She began her professional career as a do-it-all utility player at a small daily newspaper and has been frequently honored for excellence in journalism. She currently shares her office space with a surprisingly quiet beagle named Baxter.