Data Analyst

About Ferocia

We're the team behind Up—but under the hood, we're Ferocia: a passionate tech company driven by innovation and financial empowerment.

In 2011, we cut our teeth crafting the first digital platform for Bendigo Bank. Building on that foundation, we later launched Up in 2018—a reimagined banking experience designed to bring financial freedom to a generation.

By 2021, we officially became part of the Bendigo and Adelaide Bank Group, continuing our work on Up and building innovative financial software for everyone.

Sitting firmly in the sweet spot of a small company rhythm with the impact of a major player, we stay dedicated to empowering those who need it most.

As proud members of the Bendigo and Adelaide Bank Group, we're committed to being carbon neutral, community-focused, and holding ourselves to the highest standards.

The role 👤

Join our data team and help us make sense of the chaos (in the best way possible).

We’re looking for two Data Analysts who can turn messy data into clear stories, spot patterns before they become problems, and communicate insights without putting stakeholders to sleep.

You’ll work with our existing team of junior-to-mid analysts, bringing 3-5 years of battle-tested experience. You’ve seen enough dashboards to know when a metric is lying, written enough SQL to do it in your sleep, and presented enough findings to know that executives don’t want 47 slides.

This isn’t a “pull reports and call it a day” role. You’ll be:

• Digging into our product data to understand what’s actually happening vs. what we think is happening.
• Building analyses that change minds (and roadmaps).
• Partnering with product, marketing, and ops teams who depend on you to cut through the noise.
• Mentoring our junior analysts when they’re stuck (or when you need a rubber duck).

Where you’ll work: Melbourne (hybrid - in office when it matters, WFH when you need focus time)

You should apply if… ↪️

You have:

• 3-5 years as a Data Analyst or similar role - titles vary, skills matter more.
• Strong SQL skills - you can write complex queries without Stack Overflow open in another tab (most of the time). We use PostgreSQL and BigQuery.
• Statistical foundation - you understand hypothesis testing, can run and interpret A/B tests, know when correlation doesn’t imply causation, and can explain confidence intervals to non-technical stakeholders.
• Analytics tool proficiency - Tableau, Looker, Power BI, or similar. We use Metabase, but if you’re strong in one, you can learn another.
• Python or R experience - not necessarily expert level, but comfortable enough to wrangle data, run statistical tests, or build a quick model when SQL isn’t enough.
• Spreadsheet wizardry - yes, Excel/Google Sheets still matter. You know when to use a pivot table vs. when to reach for SQL.
• Business intuition - you ask “why does this matter?” before diving into analysis can define success metrics and measurement plans before feature/project launches.
• Communication skills - you can explain technical findings to non-technical humans without the jargon fog, and you can partner with engineers to ensure events/data are tracked in ways that support analysis and experimentation.

Bonus points for:

• Experience with our specific stack: PostgreSQL, BigQuery, Metabase, DBT.
• A/B testing or experimentation background.
• Experience in Finance, Product.
• Having actually used AI tools effectively.

You’ll thrive here if you:

• Like figuring out WHY something happened, not just WHAT happened.
• Get energised by “here’s a weird pattern, let me investigate” moments.
• Can handle ambiguity (our data is real-world messy, not Kaggle clean).
• Want to influence decisions, not just inform them.
• Believe good analysis is 30% technical skills, 70% asking the right questions.
• Think of AI as a copilot, not an autopilot.

This probably isn’t for you if:

• You just want to build dashboards and never talk to humans.
• Ambiguous questions stress you out instead of exciting you.
• You need every requirement spelled out in perfect detail.
• You think “the data says X” without questioning if the data might be wrong.

How we work 💪

Our Data Philosophy:

• Integrity first: Analysis should drive decisions, not justify them retroactively. We own our mistakes, ensure data accuracy, and maintain transparent reporting - even when findings aren’t what stakeholders want to hear.
• Curiosity drives insight: We don’t stop at “what happened” - we dig into “why,” explore beyond the brief, and proactively uncover patterns that weren’t explicitly asked for.
• Customer centricity guides priorities: Every analysis should ultimately connect to customer needs, pain points, or value. If we can’t explain how our work impacts customers, we question if we’re working on the right thing.
• Adaptability over perfection: Perfect is the enemy of useful - we ship 80% solutions when timing matters, learn new tools quickly, and embrace changing business needs without getting precious about our processes.
• Clarity over complexity: If you can’t explain it simply, you don’t understand it well enough. Wrong answers confidently stated are worse than “I don’t know yet, but here’s how I’ll find out”.

Our tech stack:

• Data warehouse: BigQuery.
• BI tool: Metabase.
• Transformation: DBT.
• Notebooks: Python/Jupyter, SQL editors.
• Collaboration: Slack, Notion, Figma.
• AI tools: We use them (Claude, ChatGPT, Copilot, etc.) - we expect you will too.

Team structure:

• You’ll report to the Analytics Manager.
• You’ll work closely with 3 other analysts ranging from junior to mid-level.
• You’ll partner with product managers, engineers, marketers, and ops folks regularly.

What “hybrid” actually means:

• Core collaboration days: Tuesday (in-office) are preferred for weekly team meetings.
• Deep work days: Your choice! We optimise for output, not chair time.
• Real flexibility - doctor’s appointment at 2pm? Just go. Kid’s sick? Work from home. Need to focus? Stay home.

Salary range 💰

The estimated salary range for this role is $115,000 to $145,000, exclusive of superannuation.

Our interview process 💼

We’re trying something a bit different because traditional hiring is broken:

Stage 1: Recruiter Chat (30 min)

• Get to know each other.
• We’ll ask about your DA journey and what mistakes taught you the most.
• You ask us anything (seriously - our process, our culture, why people stay or leave).

Stage 2: Take-Home Assessment (2-3 hours max) We’ll send you a real-ish business problem with messy data. Here’s what’s different:

• We care about your thinking process, not perfect code.
• You’ll submit your analysis + how you approached it + chat transcripts if you used AI.
• We’re assessing your judgment, not whether you memorised pandas syntax.

Stage 3: Technical Discussion (60 min)

• First 15 min: Present your take-home findings.
• Next 20 min: We’ll dig into your approach, ask “what if” questions, maybe do a mini live analysis together.
• Next 15 min: Technical depth - SQL problem-solving, statistical thinking, how you’d approach different scenarios.
• Last 10 min: Time for you to ask us anything!

Stage 4: Values & Stakeholder Fit (45 min)

• Meet 2 people you’d work with regularly (could be product, ops, or other people from the business).
• This isn’t about being “nice” - it’s about working style, communication, how you handle disagreement.
• We’re checking if we’d want to work with you; you’re checking if you’d want to work with us.

Stage 5: Offer (hopefully!)

• We aim to move fast - you’ll hear soon from us after each stage.
• If we’re not moving forward, we’ll tell you why (we owe you that).

Timeline: Aiming for 4-6 weeks start to finish.

FAQs 💭

Do I need to meet every requirement? No. If you’re strong in most areas and eager to learn, apply. We care more about trajectory than current perfection.

Is this role remote? Hybrid. Melbourne-based, office 1-2 days/week. Fully remote won’t work for this role (yet - we’re figuring it out).

Will I be doing a lot of reporting vs. analysis? Both. Maybe 30% regular reporting, 70% exploratory analysis and project work. The reporting you’ll own, you can (and should) automate over time.

What’s the actual career path? Mid Analyst → Senior Analyst (12-24 months) → Lead Analyst (24-36 months), depending on what you want. Or pivot to Data Science, ML, or anything else if that’s your jam.

How do you measure success in this role? Quality of insights, impact on decisions, stakeholder trust, team collaboration. Not “number of dashboards” or “queries written.”

What’s the team culture really like? Low ego, high curiosity. We have strong opinions loosely held. We debate ideas, not people. We use Slack reactions liberally.

Can I see the take-home assessment before applying? No, but it’s analysing ~2,000 rows of referral program data with some intentional messiness. Not a trick question, just realistic complexity.

Do you sponsor visas? No, we do not sponsor visas at this stage.

Working at Ferocia

We have a hybrid work culture where people can attend the Ferocia office as much or as little as makes sense for them, but we are currently only hiring in Melbourne (or adjacent areas) as we still value physically getting together at least a half-dozen times per year.

We offer:

• A small team of passionate people
• Generous leave and parental policy
• Flexible working schedule
• Great city office and perks (rooftop, gym and personal trainer, games…)
• Budget for personal development, training, and conferences
• Employee Assistance Program
• Home loan rebates for our loans (conditions apply)
• Ongoing equity grants (conditions apply)

Not quite ticking every box? Throw your hat in the ring anyway! At Ferocia, we’re all about shaking things up and rewriting the rules. We thrive on diversity and inclusion, and we wholeheartedly encourage you to step up and shine. Let us be the judge of your qualifications for this role - you just might surprise yourself!

To apply, click here. If the role is still posted, we’re still accepting applications!