Roles we recruit
Seven practices, 27 functions. Start at the practice when you cannot yet name the title; go straight to the function when you can.
Browse all 229 roles Brief us on a seatFlat 15% of first-year base · shortlist in 10 working days
Seven seats sit under data and analytics on a go-to-market team, from the data engineer who makes the numbers trustworthy to the data scientist who predicts what happens next. We split them by the stage between raw data and a decision, screen for decisions their analysis changed, and charge a flat 15% only once your hire starts.
Most analytics briefs ask for a data analyst and describe a pipeline problem, a reporting problem or a forecasting problem. We start with the decision the business cannot make today.
From raw data to a decision, and the seat that owns each stage
Moves: data freshness and trust in the numbers
1 seat
The pipelines underneath: tracking, ingestion from ad, web and CRM sources, the warehouse and the models analysts query.
Brief us when analysts spend most of the week cleaning data instead of analyzing it.
Moves: dashboard adoption and time spent building reports
1 seat
Dashboards and the reporting layer: metric definitions, data models and self-serve reports leaders actually open.
Brief us when every meeting starts with a debate about whose number is right.
Moves: channel return and conversion through each stage
2 seats
Marketing performance: channel and campaign analysis, attribution, budget return and the reporting that tells marketing what to fund next.
Brief us when marketing spends real money and cannot say which channels produce pipeline.
Hire a Marketing AnalystThe analysis behind growth: funnel conversion, cohorts, experiment design and readouts that decide what ships.
Brief us when you run tests and launches but nobody can say which ones worked, or why.
Moves: forecast accuracy and pipeline coverage
1 seat
Revenue analysis: pipeline, bookings, retention and pricing, plus the forecast inputs finance and sales leaders plan on.
Brief us when the forecast misses and nobody can explain the gap until the quarter has closed.
Moves: retention, expansion and models in use
2 seats
Models that predict: lead and account scoring, churn and expansion propensity, pricing and forecasting beyond what a spreadsheet can do.
Brief us when you have years of clean data and decisions that a model would make better and faster.
Hire a Data ScientistWho your customers are and why they buy, stay or leave: segmentation, usage analysis and survey data joined to revenue.
Brief us when product, marketing and CS each hold a different picture of the customer.
Measured on
Analytics interviews reward tool fluency: the right SQL keywords, a tidy chart, a confident walkthrough. We screen for the parts that matter in a revenue team: a decision the analysis changed, the model underneath the chart, and explaining it to someone who does not read SQL.
We ask for one analysis that changed a budget, a forecast or a roadmap, and who acted on it. Dashboards with no decision attached are where we dig.
Candidates work through a short exercise on a realistic go-to-market schema: accounts, opportunities, campaigns and touches, with the messy joins that come with them.
They present one finding to us as if we were the CMO: what it means, how sure they are and what to do next.
We ask about a time the data was wrong, how they found it and what they fixed, and check it with a former manager.
Not sure which seat you need? Tell us the question nobody can answer today and where the data lives, and we will tell you which of the seven owns it.
Public US benchmarks, read October 2026. Analyst pay is mostly base salary. Data scientists and data engineers carry more variable pay, and both sit well above analysts.
Each bar runs from the 25th to the 75th percentile, the middle half of reported pay.
Base salaryWages including commission (BLS)Median base salaryAverage total pay, base plus variable
Marketing Analyst
Salary.com base, Built In total
$84,504 to $105,186 base$102,543 average total
Business Intelligence Analyst
Salary.com base benchmark
$102,090 to $123,502 base
Data Scientist
BLS, all industries, with Built In total
$85,660 to $158,880$145,852 average total
Data Engineer
Built In, self-reported
$120,000 median base$150,234 average total
Marketing Analyst: Salary.com base salary, 25th to 75th percentile, United States, as of October 1, 2026, with Built In average total compensation (self-reported), reviewed October 5, 2026. Business Intelligence Analyst: Salary.com base salary, as of October 1, 2026. Data Scientist: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025, annual wage at the 25th and 75th percentiles (SOC 15-2051), all industries, with Built In average total compensation, reviewed October 3, 2026. Data Engineer: Built In, self-reported, reviewed October 4, 2026. Public aggregates, not ZaphyrRecruit placement data.
The clock starts when the brief is signed and stops when three to five calibrated candidates are in front of you. Most of that time is spent talking to people, not searching for them.
Day 1
Forty-five minutes with whoever the hire reports to. We leave with the number the seat owns, the range you can pay and the two or three things that genuinely disqualify.
Days 2 to 7
We build the list from people doing the job now, approach them directly and screen every name against the brief before it reaches you.
Days 8 to 10
Three to five people, each with a page: what they have run, why they fit this seat, pay expectations, notice period and where they are weak.
After the shortlist
Interviews run at your pace. We handle scheduling, references and the counter-offer conversation, and nothing is payable until your hire starts.
Three clients, quoted as they wrote to us about searches we ran for them. We publish no names or logos without permission.
A marketing analyst explains what marketing spend produced, a BI analyst builds the reporting layer everyone reads, and a data scientist builds models that predict. Analysts answer what happened and why. Data scientists answer what will happen. Most go-to-market teams need the analyst first; a data scientist pays off once the data is clean and the questions repeat.
A flat 15% of the hire’s first-year base salary, payable only once they start. On a $100,000 base that is $15,000, and the same 15% applies to data scientists and engineers. There is no retainer, no exclusivity and nothing to pay if the search does not produce a hire. If a placement leaves inside the guarantee period, we rerun the search at no further fee.
Salary.com puts marketing analysts at $84,504 to $105,186 base and business intelligence analysts at $102,090 to $123,502 (October 2026). The BLS puts the middle half of data scientists between $85,660 and $158,880 a year (May 2025), and Built In reports a $120,000 median base for data engineers. SQL depth, modeling and industry move the number.
When marketing spend is large enough that a wrong channel decision costs more than the salary. Common triggers are several paid channels with no agreed attribution, a board asking for marketing return, or marketers exporting spreadsheets every Monday. If tracking is broken, hire the data engineer or RevOps seat first, or the analyst inherits a cleanup job.
With a short SQL exercise on a realistic go-to-market schema, a walkthrough of one past analysis and a plain explanation of a finding. We look for clean joins on messy data, sensible assumptions and a recommendation, not just a chart. References confirm whether their work changed decisions, which is the part an interview cannot show.
Yes, for go-to-market work. Data scientists here build scoring, churn, propensity and forecasting models, and marketing data engineers build the tracking, pipelines and warehouse models those depend on. We brief both with your stack named, such as Snowflake, BigQuery, dbt or Python, so candidates know the tools before the first call.
Put it where the decisions are made, then give it a dotted line to the data team. A marketing analyst inside marketing hears the questions early; a central team keeps definitions consistent. Most growing B2B companies start analysts inside RevOps or marketing and form a central team once there are several, sharing one set of metric definitions.
Ten working days from a signed brief to a shortlist of three to five calibrated candidates. The SQL exercise and the walkthrough sit inside that window, so your own interviews can focus on your data and your team. Interviews and the offer then run at your pace. Most candidates then need two to four weeks before they can start.
Tell us the question the business cannot answer, where the data lives and who will act on it. We reply within one business day with which of the seven seats it is and what it pays.
Most of our analytics searches are filled from people we already know, the week an employer briefs us. Set up a job alert for operations and data roles, or send your CV and we will come to you when a seat opens.
Data & Analytics sits in our RevOps, Systems & Data practice, next to the teams that run the systems the data comes from. Research and strategy use the answers, so they are here too.