FP&A Manager

Honkforhelp; Chicago (IL); On-site; USD 90,000 - 130,000; Full time

Job summary

Honkforhelp is seeking a Financial Planning and Analysis Manager in Chicago. This role is integral to driving financial forecasts, budgeting and strategic support to senior management.

Key responsibilities include variance analysis, financial modeling, and building executive reports. The ideal candidate has 5-7 years in FP&A or corporate finance and must excel in using SQL and Python for automation.

This is an on-site position, requiring presence in the Chicago office 3-5 days a week.

Qualifications

• 5 to 7 years in FP&A, investment banking, private equity, or corporate finance.
• High-growth or PE-backed environments preferred.
• Experience with board- and lender-grade reporting.

Responsibilities

• Build and own the company’s financial forecast and annual operating plan.
• Run actual-vs-budget-vs-forecast variance analysis every month.
• Build board, lender, and exec reporting from raw data to final deck.

Skills

Financial modeling

Variance analysis

SQL

Python

Business Intelligence tools (Looker, Tableau)

Unit economics

Education

Bachelor's in finance, economics, accounting, or a quantitative field

Tools

Looker

Tableau

Python

Job description

This role will be a key asset in the future growth of the company. The Financial Planning and Analysis Manager will be driving the Company’s financial forecast, annual budgeting process, operating plan variance analysis, and special projects. This person will provide financial consulting and strategic support to senior management including preparing board presentations, financial presentations, capital expenditure analysis, industry/peer group comparisons, new business opportunities, acquisition analysis, and other projects. This function acts as the analytical engine of the company to provide insights and support ‘optimal’ business decision-making.

This position reports directly to the CFO and is expected to work from our Chicago office 3-5 days per week.

Responsibilities

• Build and own the company’s financial forecast and annual operating plan. Set the cadence, drive the inputs out of every function, and hold the business to the number.
• Run actual-vs-budget-vs-forecast variance analysis every month. Name the driver, quantify it, and explain it simply and succinctly.
• Own the integrated three-statement model, synergy model, and a rolling 13-week cash forecast.
• Turn unit economics into decisions: contribution profit by customer, market, and job type. This is the analytical engine. Use it to tell the business where to push and where to cut.
• Build board, lender, and exec reporting from raw data to final deck: KPI packages, covenant compliance, monthly financials. It ships clean and on time.
• Pressure-test new investments. Build the 80/20 ROI case; greenlight or kill on the numbers.
• Partner directly with ops, sales, and the exec team as the finance co-pilot. Not a back-office scorekeeper.
• Automate the reporting grind (Looker / Excel / Python) so your time goes to insight, not data wrangling.

Qualifications

• 5 to 7 years in FP&A, investment banking, private equity, or corporate finance. High-growth or PE-backed environments strongly preferred.
• Fluency in unit economics and contribution-margin thinking. You see a P&L as a system of drivers, not a stack of line items.
• Elite financial modeling. You build three-statement, scenario, and sensitivity models from a blank sheet. No reliance on templates.
• Board- and lender-grade reporting experience. You know what a credit agreement and a board deck demand.
• SQL, Python, and BI tools like Looker and Tableau. You either know them or you’ll learn them fast. Same goes for using Claude and other AI tools to automate the reporting grind and move faster than the team that’s still doing it by hand.
• Bachelor’s in finance, economics, accounting, or a quantitative field.
• Marketplace, on-demand, logistics, or insurance-adjacent experience a plus.
• High tolerance for speed, ambiguity, and direct feedback. You go from raw data to a recommendation without being managed to it.