Ian Klosowicz
One of the most practical questions you can ask when you're trying to break in is: where are the jobs actually concentrated? Because "data analyst" appears on job boards across every industry, and applying everywhere without a filter is a slow way to get nowhere.
The industries that hire the most entry-level data analysts are healthcare, finance and insurance, retail and e-commerce, government and public sector, technology, and consulting. Each has a different volume of openings, a different technical bar, and a different definition of what an analyst does day to day.
Here's what you need to know about each one and how to target the right one for where you're starting from.
Healthcare is one of the largest employers of entry-level data analysts in the country, and it's underrated by most people trying to break in because it doesn't feel like a "data" industry from the outside.
Hospitals, health systems, insurance companies, and public health organizations all run on data. Patient outcomes, claims processing, operational efficiency, population health, billing and coding accuracy — all of it requires analysts. And because the sector is massive and growing, entry-level openings are consistent even when tech companies are in a hiring freeze.
What the work looks like: pulling data from EHR systems and claims databases using SQL, building operational dashboards in Tableau or Power BI, reporting on patient throughput and readmission rates, and supporting compliance reporting. The domain has its own vocabulary — ICD codes, HL7, HIPAA — but most employers will teach you the domain if your SQL and Excel skills are solid.
The technical bar at entry level is lower than tech. You're not doing complex A/B testing or building event pipelines. You're answering operational questions with SQL and presenting the answers clearly. That's very achievable within a few months of focused preparation.
If you have any background in healthcare — even administrative or clinical — lead with it. Domain familiarity is a real differentiator when you're competing against candidates who only know the tools.
Finance and insurance is another high-volume sector for entry-level analyst roles, and it's more accessible than most people assume if you don't have a finance degree.
Banks, credit unions, insurance companies, and financial services firms all hire analysts across multiple functions: credit risk, operations, compliance, customer analytics, fraud detection, and FP&A. The sheer number of functions means entry-level openings exist at companies of every size, not just the large institutions.
What the work looks like: SQL is almost always required. Excel is heavy — more so than in tech. BI tools show up for dashboards. Python is less common at entry level than in tech, but it's growing, especially at fintech companies. The questions tend to be structured and quantitative: what's the default rate on this loan cohort, where is claims processing backing up, which customers are churning.
One thing worth knowing about finance: the hiring process often moves slower and has more structured HR screens than tech. Resume formatting and professional presentation matter more. The interviews lean behavioral and technical in equal measure, not purely SQL-focused the way tech analyst interviews tend to be.
If you have any accounting, economics, or math background, finance analytics is a natural target. If you don't, operations and customer analytics roles within finance companies are more accessible than credit risk or FP&A, which tend to want more specific domain knowledge.
Retail and e-commerce hire a high volume of entry-level analysts because the data is constant and fast-moving — sales, inventory, pricing, customer behavior, marketing performance — and companies need people to make sense of it at every level of the org.
Large retailers, grocery chains, and e-commerce companies all have analytics functions. At a big retailer, you might be on a merchandising analytics team tracking which products are selling, which are sitting, and where margin is leaking. At an e-commerce company, you might be on a growth team tracking conversion rates, basket size, and customer lifetime value.
What the work looks like: SQL for pulling transactional data, Excel for quick analysis and stakeholder reporting, and a BI tool for ongoing dashboards. Python shows up more at pure e-commerce and DTC brands than at traditional retailers. The pace is faster than healthcare or finance — retail analytics is responsive to real-time events like promotions, seasonality, and supply chain disruptions.
Retail is a good target if you want analyst experience quickly, especially at mid-size regional chains or e-commerce companies that don't have the brand recognition of a tech company but have real data problems and real budgets to hire for. These roles often have less competition than roles at well-known companies.
Government is consistently underestimated as an entry point for data analysts, and I think that's a mistake for a lot of people in the job search.
Federal agencies, state governments, municipal governments, and nonprofits funded by government grants all hire analysts. The Bureau of Labor Statistics, the Census Bureau, state health departments, city budget offices, transportation agencies — data work is everywhere in public sector organizations, and the hiring volume is substantial.
What the work looks like: SQL and Excel are the primary tools at most agencies. Python and R show up at more analytically mature organizations. Tableau and Power BI are common for reporting. The data problems vary wildly by agency: public health outcomes, infrastructure utilization, budget variance, crime statistics, economic indicators.
A few things that make government a genuinely good option for career changers and people without a traditional background:
The tradeoff is that government hiring is slow — sometimes very slow. Apply to federal roles expecting a 3 to 6 month process. State and local government moves faster. If you can afford the timeline, it's worth the patience.
Tech gets the most attention from people trying to break into data, and the salaries justify some of that attention. But tech is also the most competitive sector for entry-level analyst roles, and the technical bar is higher than any other industry on this list.
Software companies, SaaS businesses, platforms, and tech-adjacent companies all hire data analysts. The roles range from product analytics (understanding user behavior) to business analytics (tracking company KPIs) to revenue or sales analytics (understanding GTM performance).
What the work looks like: SQL is heavier and more complex than in most other sectors. Python comes up more often. A/B testing and experimentation knowledge matter, especially in product analytics. The questions are faster-moving and often more ambiguous — you're not just reporting on what happened, you're expected to surface insights the business didn't know to ask for.
I have 125,000 followers on LinkedIn who are mostly trying to break into data, and the pattern I see constantly is people aiming exclusively at tech companies while ignoring sectors with more openings and a lower bar. Tech is worth targeting if your SQL is solid and you have strong portfolio projects. If you're still building those fundamentals, get hired somewhere with a lower bar first, build real experience, then move to tech.
One sector worth separating out: health tech and fintech companies. They're technically in "tech" but often have more openings at the junior level than pure software companies, and the domain knowledge you build maps to adjacent industries if you want to move later.
Consulting firms — both large management consulting firms and smaller boutique shops — hire entry-level analysts in meaningful numbers, and this is another sector that gets overlooked by people who aren't already connected to it.
Large firms like Deloitte, Accenture, EY, and KPMG have entire analytics practices that hire entry-level analysts. Smaller boutique consulting firms often hire analysts to support client delivery across industries like healthcare, finance, and government. The work is client-facing, which means you build communication and presentation skills faster than in an internal analytics role.
What the work looks like: variable by project, which is the point. You might be on a healthcare data project for one client and a supply chain analysis for another. The tools vary with the client, but SQL, Excel, and a BI tool are the baseline. The pace is high and the expectations for structured communication are higher than most internal analyst roles.
Consulting is a good fit if you want broad exposure across industries and problems quickly, and if you're comfortable with the client-facing, structured-deliverable style of work. It's a harder lift interpersonally than an internal role — you're presenting to clients, not just internal stakeholders — but the skill development is fast.
The Analyst Hive program covers the communication and presentation skills that consulting-style roles expect, not just the technical SQL and BI work. Both matter at the analyst level, but in consulting, the ability to present a finding clearly is weighted more heavily than in most internal roles.
A few practical takeaways from this breakdown:
Match the sector to your existing background. If you have 5 years in healthcare administration, healthcare analytics is the fastest path in. If you've worked in retail, retail analytics lets you skip the domain learning curve. Your prior experience is a filter, not a liability.
Don't only apply to tech. Tech has the highest salaries and the most competition. Healthcare, government, and retail have more openings at entry level and a lower technical bar. Getting hired somewhere else first and moving to tech later is a real and valid path — and it's often faster than waiting to be competitive for a tech role.
The tools required are consistent across sectors. SQL, Excel, and a BI tool get you into every industry on this list. The domain knowledge is different, but the technical foundation is the same. Build the foundation first, then target the sector that fits your background.
Apply to mid-size companies, not just household names. A regional hospital system, a mid-size insurance company, or a national retailer you've never heard of will have real analyst roles with real data problems and far less competition than the well-known employers everyone is applying to. Those jobs count the same on your resume.
Which industry pays entry-level data analysts the most?
Technology companies pay the most at entry level, often $80,000 to $100,000 or higher at well-funded startups and large tech firms. Finance and insurance come next, followed by consulting. Healthcare and government pay solid salaries but typically fall below tech compensation, especially when factoring in total comp. The tradeoff is that tech and finance roles are harder to break into without a strong technical profile.
Is healthcare really a good place to start a data analyst career?
Yes, and it's one of the most underrated entry points. The volume of openings is high, the technical bar is achievable with a SQL and Excel foundation, job stability is strong, and the domain knowledge you build is transferable within the sector. The pay is solid without being as high as tech, but for someone breaking in without a traditional background, it's a realistic and sustainable starting point.
Do government data analyst jobs require a degree?
Many federal and state government postings list a degree as a requirement, but experience and demonstrated skills can often substitute. Some agencies have explicit provisions for substituting years of experience for a degree. Local and municipal government roles vary. It's worth reading the job posting carefully rather than self-selecting out — the structured criteria can actually work in your favor if you meet the skill requirements even without the credential.
Is it worth starting at a non-tech company if I want to work in tech eventually?
Yes. Getting hired as an analyst in healthcare, retail, or finance builds real SQL and analysis experience that tech companies recognize. Most tech companies care that you can do the work, not where you first learned to do it. Starting outside tech, building 1 to 2 years of experience, and then applying to tech roles is faster for most people than waiting until they're competitive for a tech role right out of the gate.
Which sector has the most remote data analyst jobs?
Tech has the highest proportion of remote roles, followed by consulting (which often has a hybrid model). Healthcare has a mix — roles supporting clinical operations tend to be on-site, while health insurance and health tech roles are more likely to be remote or hybrid. Government remote availability varies by agency and level. Finance is largely in-office at traditional institutions, more flexible at fintech companies.
The path in is the same regardless of which industry you target: SQL, Excel, a BI tool, and a portfolio that shows you can answer real business questions with data. Analyst Hive is a 90-day program that builds exactly that — the assets, the skills, and the interview prep to get hired. Month 1 builds the foundation. Month 2 sharpens it. Month 3 gets you into interviews and negotiations.
