Most In-Demand Skills in Fintech in 2026: Which Skills Appear Together

Payments is the most common skill tag in Finjobsly's job data, and unlike in the 1 October edition it stays on top once the largest employer is removed. As of 20 October 2026, Finjobsly tracked 4,187 active roles at 36 employers, and 4,173 of them carry skill tags. Payments appears on 1,967 of those (47.1%). Excluding Stripe, which supplies 776 of the tagged roles, Payments falls to 35.9% of the remaining 3,397 roles and Compliance rises to 27.7%, so the gap narrows from 21.1 to 8.2 percentage points. Compliance is the only tag present at all 36 employers. This article looks at skills as combinations rather than a league table: which tags travel together, which are distinctive to each job function, and where advertised pay differs between roles with and without a skill. It is not comparable with the 1 October edition, because the panel has grown from 33 to 36 employers and from 2,692 to 4,187 roles.

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Key findings

• Of 4,173 tagged roles at 36 tracked employers (20 October 2026), Payments appears on 1,967 (47.1%), Compliance on 1,085 (26.0%), Go on 982 (23.5%), Python on 702 (16.8%) and SQL on 613 (14.7%). Stripe is 776 of the 4,173 roles (18.6%).
• Excluding Stripe, Payments falls from 47.1% to 35.9% of roles while Compliance rises from 26.0% to 27.7%. Stripe tags Payments on 749 of its 776 roles, so it lifts the headline figure, but Payments still leads without it. Compliance is tagged at all 36 employers and Payments at 31.
• The most common pairs, leaving out Go, are Payments with Compliance (497 roles), Python with SQL (318), Payments with SaaS (307) and Payments with SQL (300). Infrastructure pairs are almost independent of Stripe (AWS with Kubernetes: 212 of 229 roles are at other employers), while Payments pairs are not.
• Compliance is not only a compliance skill. It is tagged on 37.6% of product and design roles (152 of 404) and 22.0% of engineering roles (240 of 1,091) in the panel.
• The Go tag cannot be read as the programming language outside engineering: it appears on 262 sales and go-to-market roles, 97 of them on account executive titles. Treat it as reliable only within engineering (327 of 1,091 roles).
• Advertised pay gaps by skill are mostly small and partly reflect employer mix. The exception is data and machine learning, where roles tagged Machine Learning advertise a $195,500 median minimum (n=40) against $149,500 without it (n=44).

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Which skills appear most often in fintech job ads?

Skill counts use the skill tags on each active role. A role can carry several tags, so the figures do not sum to 100%. The first denominator is the 4,173 roles with at least one tag. The second is the 3,397 tagged roles that are not at Stripe, shown because one employer is 19% of the panel and tags almost all of its roles with Payments and many with Go.

The practical reading is that Payments is the most common label but Compliance is the broader signal. Stripe tags Go on 296 of its 776 roles and Payments on 749. Without Stripe, Salesforce (15.0%) and REST (13.8%) both rank above SQL (13.4%), which suggests commercial and API skills are a bigger part of the non-Stripe panel than the headline table implies.

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How are fintech skills grouped by type of work?

To avoid reading single tags in isolation, tags were grouped into five families (the lists are in the methodology). The table counts roles with at least one tag from the group, on the same two denominators.

Risk and compliance skills are now on more roles than technical skills (1,528 against 1,433). For a sector often described through its engineering, that is the most useful number for a career changer: more fintech job ads ask for risk and compliance knowledge than for a technical stack. Data and AI skills sit on 22.5% of roles, and the share is slightly lower when Stripe is removed. The regtech and compliance hiring guide goes deeper on that side of the market.

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Which fintech skills are most often listed together?

Pair counts are the number of roles carrying both tags, with the count outside Stripe alongside. A pair that stays high without Stripe is a more reliable signal of how employers describe jobs.

The Go and Payments pair is the largest in the data (611 roles) but is excluded from the table because the Go tag is unreliable outside engineering. Restricted to the engineering family, Go and Payments appear together on 153 roles. Three patterns are clear. First, Python and SQL is the standard data pairing: 163 of the 318 roles carrying both are in the data and machine learning family, and the pair is spread across 30 employers. Second, AWS with Kubernetes, AWS with Python and Python with REST are infrastructure pairs where 212, 252 and 192 of the roles are outside Stripe, so they describe how employers broadly write engineering jobs. Third, Compliance pairs with Payments on 497 roles but also with the profession-specific tags Risk Management (181), AML (152) and KYC (117), so it reads as a professional discipline as well as a product label. For the engineering stack in more depth see skills and languages fintech engineers are screened for.

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Which skills define each fintech job function?

Distinctiveness compares the share of roles in a function carrying a skill with the share across all tagged roles. A multiple of 3.0x means the skill is three times as common in that function as in the panel. Only skills on at least 15 roles in the function and at 5 or more employers are shown, up to the three highest multiples of 2.0x or more per function (ties are shown). Functions come from title rules described in the methodology, and the denominator is the roles with skill tags in each function. Marketing has no skill that meets these thresholds.

The surprise is how far Compliance travels. It does not make the distinctive list for product and design roles, but it still sits on 37.6% of them (152 of 404 at 27 employers), 1.4 times the panel rate, and on 22.0% of engineering roles (240 of 1,091 at 28 employers). For a product manager or engineer, regulatory fluency is being written into the job description rather than left to a separate team. Python is also carried by 67.1% of data and machine learning roles but only 3.1% of sales and go-to-market roles (29 of 925), so it separates functions cleanly, while SQL appears at 68.5% of data roles and 16.8% of risk roles. See the data analyst salary guide for how the analyst path is priced.

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Do roles that list a skill advertise higher pay?

Sometimes, but the differences are modest and not consistent, so they should not be read as a skill premium. The comparison below uses USD-paid roles with a usable advertised range (minimum of at least $20,000 and a maximum not below the minimum), compares roles inside the same function with and without a skill, and is shown only where both sides have 20 or more roles. Values are medians of the advertised minimum. The comparison covers 382 engineering roles, 84 data and machine learning roles, 72 risk, compliance and legal roles, 143 product and design roles and 136 sales and go-to-market roles. Only a selection of the qualifying skills is shown.

Two readings hold up. In engineering, Payments, AWS, Kubernetes, Java and Go tags sit about $1,000 to $7,000 above the comparison group, which is small, while Python, Terraform, Ruby and Salesforce tags sit below theirs, which is consistent with those tags being baseline requirements that also appear on junior and analyst roles. The largest gap is in data and machine learning, where roles tagged Machine Learning advertise a $195,500 median minimum against $149,500 without it, and SQL runs the other way for the same reason as Python. In every case the sample is a few employers advertising USD ranges, and a skill tag can proxy for seniority or employer, so no causal claim is made. To check a specific offer, use the Salary Benchmark.

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What should a candidate do with this?

• Pair skills the way employers do. A Python and SQL candidate matches the most common data pairing; adding Machine Learning moves into the role family where that tag is 7.9x more common than average.
• Do not skip regulatory vocabulary. Compliance is the second most common tag overall, the only one at all 36 employers, and sits on more than a third of product and design roles.
• Read Go carefully. A Go tag on a sales listing is not a programming requirement.

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Current opportunities

Search live roles by function and city: software engineering in London, software engineering in New York, data in London, data in New York and risk, compliance and legal in London. The full inventory is on the fintech jobs page, and the Finjobsly Fintech Hiring Index gives panel-level context.

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Methodology

All figures come from Finjobsly's live job database (active roles only) as of 20 October 2026: 4,187 active roles at 36 tracked employers, of which 4,173 have at least one skill tag. This is Finjobsly's panel and not the whole fintech market. Skill tags are extracted from job content, compared case-insensitively and counted once per role; the average tagged role carries 4.4 tags. Stripe is 776 of the 4,173 tagged roles (18.6%), so key figures are shown with and without it. Skill groups are defined by explicit tag lists. Technical: Python, Java, REST, AWS, Kubernetes, React, CI/CD, TypeScript, Ruby, Terraform, Kafka, JavaScript, GCP, MySQL, GraphQL, PostgreSQL, Azure, Docker, Rust, Redis, Spring, Kotlin, Scala, Microservices (Go is excluded because of the tagging issue described above). Data and AI: SQL, Data Science, Machine Learning, Tableau, Data Engineering, Data Analysis, Power BI, AI Tools, Generative AI, Data Analytics, Data Modeling. Commercial: Salesforce, SaaS, Customer Success, Account Management, Negotiation, B2B Sales, Pipeline Management, HubSpot, Business Development, Sales, Consultative Selling, Contract Negotiation. Risk and compliance: Compliance, Fraud, Risk Management, AML, KYC, Sanctions, Credit Risk, Regulatory Reporting, Regulatory Compliance, SOX, PCI DSS, Underwriting. Product and delivery: Product Management, Agile, Stakeholder Management, Project Management, Scrum, Figma, User Research. Pay comparisons use USD roles with a minimum of at least $20,000 and a maximum not below the minimum, compare within function, and are shown only when both sides have n of 20 or more. Advertised ranges are not salaries paid. No trend claims are made because the panel was assembled in July 2026, and these figures are not comparable with the 1 October edition because the panel has grown.

Role families are assigned from job titles using one rule applied across Finjobsly articles, in this order: data and machine learning (data scientist, machine learning, data or analytics engineer, data and business analyst, BI, analytics, and fraud, risk or credit modelling titles), sales and go-to-market (sales, account executive, business development, SDR or BDR, partnerships, customer success, account management, solutions engineering and solutions architect), operations and support (any title containing support), engineering (engineer, developer, software, SRE, DevOps, architect), product and design, risk, compliance and legal, marketing, finance, people, and operations. Titles that match none are counted as other. Title matching is imperfect, so a few roles may sit in a neighbouring family. In this article, function means role family, and the function tables count roles with at least one skill tag in each family.

Last updated: 20 October 2026.

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