Motive·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Motive gave me a pretty demanding technical screen that was basically 'build a real data pipeline from scratch, live, while narrating your thought process.' More moving parts than I expected for what I thought would be a standard coding round.

Questions Asked (1)

Set up a minimal coding environment from scratch and write runnable code that fetches data from a REST API endpoint returning fleet/courier tracking records. Extract specific fields, handle auth, pagination, rate limiting, timeouts, and malformed records, then output both serialized records (CSV or JSON) and per-category aggregates for the top-k items. Walk through your approach as you go and discuss edge cases and complexity.

API & IntegrationsAlgorithms & Data StructuresSystem Design

Author's notes

This was basically a full mini-project crammed into one question.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and assumptions, then outline a modular design that separates concerns: HTTP client with auth, pagination, retry/backoff, and timeout handling; data parsing and validation; aggregation; and output serialization. Implement a minimal but runnable Python script using requests and standard libraries, walking through each component while discussing edge cases and complexity.

Pro tip: Before coding, explicitly state your assumptions (e.g., auth type, pagination style, rate limit headers) and ask clarifying questions—this shows you think about real-world integration challenges and avoids building the wrong thing.

1. Clarify requirements and assumptions

Ask about auth method (API key, OAuth), pagination (cursor, offset, link headers), rate limit headers, expected data volume, and output format preferences. State your assumptions if details are missing.

2. Design modular components

Outline a structure: a client class handling auth, timeouts, retries with exponential backoff, and pagination; a parser that validates and extracts fields; an aggregator for top-k per category; and a serializer for CSV/JSON output.

3. Implement fetching with resilience

Write code to fetch pages, handle 401/403 by refreshing tokens if needed, respect Retry-After headers, use timeouts, and catch network errors. Use a session for connection reuse.

4. Process and validate records

Parse JSON, skip malformed records with logging, extract required fields (e.g., courier_id, status, timestamp, location), and handle missing or type-mismatched values gracefully.

5. Aggregate and output

Compute per-category counts or sums, find top-k items using a heap or sorting, and serialize both raw records and aggregates to CSV/JSON. Discuss time/space complexity.

Key Points to Mention

Authentication: API key in header, OAuth token refresh, or basic auth; handle 401/403 responses.
Pagination: cursor-based, offset/limit, or link headers; loop until no more pages, with a safety max page limit.
Rate limiting: respect Retry-After header, implement exponential backoff with jitter, and consider client-side throttling.
Timeouts and retries: set connect/read timeouts, retry on transient errors (5xx, 429) with backoff, and use idempotent requests.
Malformed records: validate schema, skip invalid entries with logging, and ensure aggregation doesn't crash on bad data.
Aggregation and top-k: use a min-heap of size k for O(n log k) time, or sort if k is large; discuss trade-offs.
Output serialization: use csv module or json.dumps, handle special characters, and consider streaming for large datasets.
Complexity analysis: fetching O(n) pages, processing O(n) records, aggregation O(n log k) with heap, overall O(n log k) time and O(k) space for top-k.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.