JP Morgan Chase Data Analyst Interview Guide

From what candidates report, the question that separates JPMC data analyst finalists from rejects isn't a tricky window function. It's whether you can explain why a shift in deposit mix matters when the Fed moves rates. Banking domain fluency is the silent filter in this process, and most people don't realize it until they're already in the final round.

JP Morgan Chase Data Analyst Role

At JPMC, a data analyst owns the numbers that business leaders cite in weekly reviews and that compliance teams audit quarterly. You'll pull transaction-level data from internal warehouses, reconcile it across upstream systems, and build dashboards in Tableau (and yes, Excel workbooks with pivot tables for the Managing Directors who prefer them). After year one, success looks like owning a recurring reporting cadence end-to-end: the SQL, the visualization, the documented methodology that an auditor can follow without asking you a single question.

A Typical Week

The surprise in that breakdown is how much of your week is spent writing documentation and sitting in stakeholder meetings rather than heads-down coding. If you're coming from a tech company where "data analyst" means living in a notebook, the regulatory documentation load and the Monday-through-Friday meeting cadence will feel like a different job entirely.

Projects & Impact Areas

Consumer Banking analysts might spend a week building branch performance scorecards, then pivot to analyzing conversion lift on a Chase Sapphire campaign segmented by income tier and geography. CIB work has a completely different texture: trade surveillance reporting and regulatory data pulls where a misjoined table doesn't just look bad in a dashboard, it can trigger a compliance finding. Reference data analysts validate security master data and Standard Settlement Instructions, a less visible role where a single bad record can misdirect a large wire transfer.

Skills & What's Expected

The overrated skill for this role is machine learning. Candidates who lead with scikit-learn projects are signaling they misread the job posting. SQL is the primary tool, and Python appears for occasional one-off analyses in Jupyter Notebooks, not as the daily workhorse. The underrated skill is business acumen: knowing that "active checking account" means different things in Chase retail versus private banking, or being able to discuss deposit flows without glazing over.

Levels & Career Growth

Entry points are the Data Science Analyst Program (new grads) and Associate-level lateral hires. The Associate-to-VP jump is where many data analysts stall, because VP at JPMC means driving stakeholder strategy, not just delivering analyses. Internal mobility is the real upside of the firm's scale: you can move from Consumer Banking to CIB to Asset Management without changing employers, and early-career programs actively encourage rotations.

Work Culture

The culture notes in the data say five days in-office at most locations as of 2025 with badge tracking, though the official job descriptions sometimes describe flexibility varying by team and manager. In practice, plan for full in-office. The pace is steady corporate (think 8:30 to 5:30 with late pushes around month-end), and process is heavy: production dashboards go through formal change management, and every metric shown to leadership needs documented lineage in Confluence.

JP Morgan Chase Data Analyst Compensation

Base salary carries most of the weight here, not equity. RSUs may factor in for more senior roles, but the source data suggests they're not a guaranteed part of the package at every level. Your annual bonus is performance-based, tied to both how you perform and how the company performs. That makes total comp less predictable year to year than a fixed equity vesting schedule, but it also means there's no cliff or back-loading to stress about.

On negotiation: the source data indicates base salary has some flexibility, so don't assume the first number is final. Come prepared with market rates for data analysts in financial services and make a specific case grounded in your experience. Rather than fixating on base alone, push the conversation toward total compensation, because the bonus component and any sign-on offer can meaningfully change the math. Practice framing your ask around the value you bring to JPMC's specific priorities (branch expansion analytics, regulatory reporting, deposit flow analysis) rather than generic "I deserve more."

JP Morgan Chase Data Analyst Interview Process

Budget about five weeks from application to offer, though gaps between rounds can stretch without explanation. Offer letters themselves can take one to two weeks to arrive after your final round, per candidate reports on financial services forums. Don't interpret silence as rejection. Follow up with your recruiter if you haven't heard anything after two weeks.

The most commonly cited rejection reasons cluster around two gaps: insufficient SQL depth and poor communication of business context. Interviewers want you to ask about table grain (one row per transaction? per daily balance?) before you start writing joins on JPMC's banking schemas. A technically correct query that ignores the financial logic behind the data, like confusing gross vs. net charge-offs, signals you'll need too much hand-holding in a role where regulatory accuracy is non-negotiable.

JP Morgan Chase Data Analyst Interview Questions

/* Latest trade version per trade_id as of a report timestamp, excluding cancelled */
WITH params AS (
  SELECT TIMESTAMP '2026-01-31 23:59:59' AS report_ts
),
ranked AS (
  SELECT
    t.trade_id,
    t.trade_version,
    t.event_ts,
    t.status,
    t.product,
    t.notional_amount,
    t.currency,
    t.counterparty_id,
    ROW_NUMBER() OVER (
      PARTITION BY t.trade_id
      ORDER BY t.event_ts DESC, t.trade_version DESC
    ) AS rn
  FROM trades t
  CROSS JOIN params p
  WHERE t.event_ts <= p.report_ts
)
SELECT
  trade_id,
  trade_version,
  event_ts,
  status,
  product,
  notional_amount,
  currency,
  counterparty_id
FROM ranked
WHERE rn = 1
  AND status <> 'CANCELLED';

/* Active SSI per (client_id, currency) as of a value_date, plus an overlap flag per pair */
WITH params AS (
  SELECT DATE '2026-02-15' AS value_date
),
ssi_norm AS (
  SELECT
    s.client_id,
    s.currency,
    s.ssi_id,
    s.effective_from,
    COALESCE(s.effective_to, DATE '9999-12-31') AS effective_to
  FROM ssi_instructions s
),
ordered AS (
  SELECT
    n.*, 
    LAG(n.effective_to) OVER (
      PARTITION BY n.client_id, n.currency
      ORDER BY n.effective_from, n.ssi_id
    ) AS prev_effective_to,
    ROW_NUMBER() OVER (
      PARTITION BY n.client_id, n.currency
      ORDER BY n.effective_from DESC, n.ssi_id DESC
    ) FILTER (WHERE n.effective_from <= (SELECT value_date FROM params)) AS rn_asof
  FROM ssi_norm n
),
overlap_flags AS (
  SELECT
    client_id,
    currency,
    MAX(
      CASE
        WHEN prev_effective_to IS NOT NULL AND effective_from <= prev_effective_to THEN 1
        ELSE 0
      END
    ) AS has_overlap
  FROM ordered
  GROUP BY client_id, currency
),
active_ssi AS (
  SELECT
    o.client_id,
    o.currency,
    o.ssi_id,
    o.effective_from,
    o.effective_to
  FROM ordered o
  CROSS JOIN params p
  WHERE o.effective_from <= p.value_date
    AND p.value_date <= o.effective_to
  QUALIFY ROW_NUMBER() OVER (
    PARTITION BY o.client_id, o.currency
    ORDER BY o.effective_from DESC, o.ssi_id DESC
  ) = 1
)
SELECT
  a.client_id,
  a.currency,
  a.ssi_id,
  a.effective_from,
  NULLIF(a.effective_to, DATE '9999-12-31') AS effective_to,
  f.has_overlap
FROM active_ssi a
JOIN overlap_flags f
  ON f.client_id = a.client_id
 AND f.currency = a.currency;

The chart above tells you where the weight sits, but here's what it can't show: behavioral questions at JPMC aren't generic "tell me about a time" prompts. They're scenario-driven and laced with banking operations context (SSI breaks, margin call discrepancies, KYC audit trails), so you need financial fluency just to understand what's being asked, let alone answer well. The biggest prep mistake is treating SQL practice and domain knowledge as separate workstreams when JPMC's interviewers deliberately blend them, asking you to query a trades table with late corrections one minute, then explain why that correction matters for a CCAR submission the next.

Build that combined muscle at datainterview.com/questions, where you can practice with settlement, onboarding, and collateral-style scenarios that mirror real JPMC interview patterns.

How to Prepare for JP Morgan Chase Data Analyst Interviews

Chase is pushing to open more than 160 new branches in over 30 states in 2026, aiming for 75% of Americans to be within a reasonable drive of a location. That's happening alongside revenue of ~$168 billion and a workforce that grew 2.4% year-over-year to over 318,000. For data analysts, this means the questions you'll answer on the job are tied to real, measurable bets the firm is making right now: new-market deposit capture, branch performance benchmarking, customer acquisition in states where Chase previously had no physical presence.

The "why JPMC" answer that falls flat is any version of "I want to work at a top bank." Tie yours to a specific strategic priority instead. The firm's 2025 Investor Day presentation highlights financial health and wealth creation as focus areas, which translates directly into new KPIs and dashboards a data analyst would own. Reference that, or the branch expansion math, and you'll sound like someone who's done the homework.

Try a Real Interview Question

| trade_id | trade_date | client_id | product_type | notional_usd | status    |
|----------|------------|-----------|--------------|--------------|-----------|
| 1001     | 2025-01-03 | 1         | IRS          | 1200000      | BOOKED    |
| 1002     | 2025-01-15 | 2         | FXSPOT       | 250000       | BOOKED    |
| 1003     | 2025-01-20 | 3         | CDS          | 800000       | CANCELED  |
| 1004     | 2025-02-02 | 1         | FXFWD        | 600000       | BOOKED    |
| 1005     | 2025-02-18 | 3         | IRS          | 1500000      | BOOKED    |

| client_id | client_name     | segment |
|-----------|------------------|---------|
| 1         | Apex Capital     | HEDGE   |
| 2         | Beacon Pension   | PENSION |
| 3         | Cedar Insurance  | INSURE  |

| trade_id | breach_type | breach_flag |
|----------|-------------|-------------|
| 1001     | KYC         | 1           |
| 1002     | SANCTIONS   | 0           |
| 1004     | LIMIT       | 1           |
| 1005     | KYC         | 0           |
| 9999     | KYC         | 1           |

WITH base AS (
  SELECT
    to_char(date_trunc('month', t.trade_date), 'YYYY-MM') AS month,
    c.segment,
    t.trade_id,
    CASE
      WHEN COALESCE(b.breach_flag, 0) = 1 THEN 1
      ELSE 0
    END AS is_breach
  FROM trades t
  JOIN clients c
    ON c.client_id = t.client_id
  LEFT JOIN breaches b
    ON b.trade_id = t.trade_id
  WHERE t.status = 'BOOKED'
)
SELECT
  month,
  segment,
  COUNT(*) AS total_trades,
  SUM(is_breach) AS breaches,
  ROUND(SUM(is_breach)::numeric / NULLIF(COUNT(*), 0), 4) AS breach_rate
FROM base
GROUP BY month, segment
ORDER BY month, segment;

JPMC's data analyst interviews lean heavily on SQL applied to banking-shaped data, so practicing on generic e-commerce schemas won't fully prepare you. Build comfort with financial transaction tables, date-range filtering, and period-over-period comparisons on datainterview.com/coding.

Test Your Readiness

Use your results to prioritize what to study next, then close the gaps with targeted drills at datainterview.com/questions.