How to Get a Senior Engineering Job at Google in 2026: Resume, Interview, and Tips

import re
import json
import sys
import os
from typing import Dict, List, Tuple
import nltk
from transformers import BertTokenizer, BertForSequenceClassification
import torch

# Download required NLTK data (run once)
try:
    nltk.data.find('tokenizers/punkt')
except LookupError:
    nltk.download('punkt')

# Google 2026 Senior Engineering Resume Rubric Keywords
RUBRIC_KEYWORDS = {
    'system_design': ['distributed systems', 'kubernetes', 'grpc', 'spanner', 'load balancing', 'p99 latency', 'scalability', 'fault tolerance', 'caching'],
    'coding': ['leetcode', 'optimization', 'time complexity', 'edge cases', 'unit testing', 'ci/cd', 'algorithm design'],
    'impact': ['quantified results', 'cost savings', 'revenue growth', 'user growth', 'p99 reduction', 'throughput increase'],
    'open_source': ['github', 'merged PR', 'open-source contribution', 'maintainer', 'stars', 'upstream contribution']
}

# Load pre-trained BERT model for ATS compatibility check
def load_bert_model() -> Tuple[BertTokenizer, BertForSequenceClassification]:
    try:
        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
        model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
        model.eval()
        return tokenizer, model
    except Exception as e:
        print(f'Error loading BERT model: {e}', file=sys.stderr)
        sys.exit(1)

# Parse resume text from plain text file
def parse_resume(file_path: str) -> str:
    if not os.path.exists(file_path):
        raise FileNotFoundError(f'Resume file not found at {file_path}')
    try:
        with open(file_path, 'r', encoding='utf-8') as f:
            return f.read().lower()
    except UnicodeDecodeError:
        # Fallback to latin-1 encoding if utf-8 fails
        with open(file_path, 'r', encoding='latin-1') as f:
            return f.read().lower()

# Check resume against rubric keywords
def check_rubric_compliance(resume_text: str) -> Dict[str, List[str]]:
    compliance = {category: [] for category in RUBRIC_KEYWORDS}
    tokens = nltk.word_tokenize(resume_text)
    for category, keywords in RUBRIC_KEYWORDS.items():
        for keyword in keywords:
            if keyword in tokens or keyword in resume_text:
                compliance[category].append(keyword)
    return compliance

# Calculate ATS pass probability using BERT
def calculate_pass_probability(resume_text: str, tokenizer: BertTokenizer, model: BertForSequenceClassification) -> float:
    inputs = tokenizer(resume_text, return_tensors='pt', truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    probabilities = torch.softmax(outputs.logits, dim=1)
    return probabilities[0][1].item() * 100  # Probability of "pass" class

def main():
    if len(sys.argv) != 2:
        print('Usage: python resume_parser.py ', file=sys.stderr)
        sys.exit(1)
    resume_path = sys.argv[1]
    try:
        resume_text = parse_resume(resume_path)
    except FileNotFoundError as e:
        print(e, file=sys.stderr)
        sys.exit(1)

    print('Loading BERT model for ATS compatibility check...')
    tokenizer, model = load_bert_model()

    print('Checking rubric compliance...')
    compliance = check_rubric_compliance(resume_text)

    print('Calculating ATS pass probability...')
    pass_prob = calculate_pass_probability(resume_text, tokenizer, model)

    # Output results
    print('\n=== Resume Audit Results ===')
    print(f'ATS Pass Probability: {pass_prob:.1f}%')
    print('\nRubric Compliance:')
    for category, keywords in compliance.items():
        print(f'  {category}: {len(keywords)}/{len(RUBRIC_KEYWORDS[category])} keywords found')
        if keywords:
            print(f'    Found: {", ".join(keywords)}')
    print('\nRecommendations:')
    for category, keywords in compliance.items():
        missing = [k for k in RUBRIC_KEYWORDS[category] if k not in keywords]
        if missing:
            print(f'  Add {category} keywords: {", ".join(missing)}')
    if pass_prob < 30:
        print('\nWARNING: Pass probability is below 30% – revise resume before applying.')
    elif pass_prob < 50:
        print('\nNOTE: Pass probability is below 50% – add more quantified impact statements.')

if __name__ == '__main__':
    main()

import flask
from flask import Flask, request, jsonify
import sqlite3
import datetime
import os
from typing import Dict, List, Optional

app = Flask(__name__)
DATABASE = 'prep_tracker.db'

# Initialize SQLite database for tracking progress
def init_db():
    try:
        conn = sqlite3.connect(DATABASE)
        cursor = conn.cursor()
        # Create problems table
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS problems (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                leetcode_id INTEGER NOT NULL,
                title TEXT NOT NULL,
                difficulty TEXT NOT NULL,
                time_complexity TEXT,
                runtime_ms INTEGER,
                completed_date TEXT,
                notes TEXT
            )
        ''')
        # Create study_sessions table
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS study_sessions (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                session_date TEXT NOT NULL,
                duration_minutes INTEGER NOT NULL,
                problems_completed INTEGER NOT NULL
            )
        ''')
        conn.commit()
        conn.close()
    except sqlite3.Error as e:
        print(f'Database initialization error: {e}', file=sys.stderr)
        sys.exit(1)

# Connect to database
def get_db() -> sqlite3.Connection:
    try:
        conn = sqlite3.connect(DATABASE)
        conn.row_factory = sqlite3.Row
        return conn
    except sqlite3.Error as e:
        print(f'Database connection error: {e}', file=sys.stderr)
        return None

# API endpoint to add a completed problem
@app.route('/api/problems', methods=['POST'])
def add_problem():
    data = request.get_json()
    required_fields = ['leetcode_id', 'title', 'difficulty', 'runtime_ms']
    for field in required_fields:
        if field not in data:
            return jsonify({'error': f'Missing required field: {field}'}), 400

    # Validate difficulty
    if data['difficulty'] not in ['Easy', 'Medium', 'Hard']:
        return jsonify({'error': 'Difficulty must be Easy, Medium, or Hard'}), 400

    # Validate runtime
    if not isinstance(data['runtime_ms'], int) or data['runtime_ms'] < 0:
        return jsonify({'error': 'Runtime must be a non-negative integer'}), 400

    conn = get_db()
    if not conn:
        return jsonify({'error': 'Database connection failed'}), 500
    try:
        cursor = conn.cursor()
        cursor.execute('''
            INSERT INTO problems (leetcode_id, title, difficulty, time_complexity, runtime_ms, completed_date, notes)
            VALUES (?, ?, ?, ?, ?, ?, ?)
        ''', (
            data['leetcode_id'],
            data['title'],
            data['difficulty'],
            data.get('time_complexity'),
            data['runtime_ms'],
            datetime.datetime.now().strftime('%Y-%m-%d'),
            data.get('notes')
        ))
        conn.commit()
        return jsonify({'message': 'Problem added successfully', 'id': cursor.lastrowid}), 201
    except sqlite3.Error as e:
        return jsonify({'error': f'Database error: {e}'}), 500
    finally:
        conn.close()

# API endpoint to get progress summary
@app.route('/api/progress', methods=['GET'])
def get_progress():
    conn = get_db()
    if not conn:
        return jsonify({'error': 'Database connection failed'}), 500
    try:
        cursor = conn.cursor()
        # Get total problems by difficulty
        cursor.execute('''
            SELECT difficulty, COUNT(*) as count, AVG(runtime_ms) as avg_runtime
            FROM problems
            GROUP BY difficulty
        ''')
        progress = {}
        for row in cursor.fetchall():
            progress[row['difficulty']] = {
                'count': row['count'],
                'avg_runtime_ms': round(row['avg_runtime'], 2) if row['avg_runtime'] else 0
            }
        # Get total study time
        cursor.execute('SELECT SUM(duration_minutes) as total_minutes FROM study_sessions')
        total_minutes = cursor.fetchone()['total_minutes'] or 0
        return jsonify({
            'progress': progress,
            'total_study_minutes': total_minutes,
            'total_study_hours': round(total_minutes / 60, 2)
        }), 200
    except sqlite3.Error as e:
        return jsonify({'error': f'Database error: {e}'}), 500
    finally:
        conn.close()

if __name__ == '__main__':
    init_db()
    app.run(debug=True, port=5000)

import markdown
import json
import os
import sys
from typing import Dict, List
from datetime import datetime

# Google 2026 System Design Doc Template
DOC_TEMPLATE = """# System Design Document: {title}
**Author:** {author}
**Date:** {date}
**Status:** Draft
**Related Rubric Items:** {rubric_items}

## 1. Requirements
### 1.1 Functional Requirements
{functional_reqs}

### 1.2 Non-Functional Requirements
{non_functional_reqs}

## 2. Capacity Estimation
{capacity_estimation}

## 3. High-Level Design
### 3.1 Architecture Diagram
{architecture_diagram}

### 3.2 Component Breakdown
{component_breakdown}

## 4. Database Design
### 4.1 Schema
{db_schema}

### 4.2 Sharding/Replication Strategy
{sharding_strategy}

## 5. Scalability & Reliability
### 5.1 Load Balancing
{load_balancing}

### 5.2 Caching Strategy
{caching_strategy}

### 5.3 Fault Tolerance
{fault_tolerance}

## 6. Tradeoffs
{tradeoffs}

## 7. Monitoring & Alerting
{monitoring}
"""

def load_input_file(file_path: str) -> Dict:
    if not os.path.exists(file_path):
        raise FileNotFoundError(f'Input file not found at {file_path}')
    try:
        with open(file_path, 'r', encoding='utf-8') as f:
            return json.load(f)
    except json.JSONDecodeError as e:
        raise ValueError(f'Invalid JSON in input file: {e}')

def validate_input(input_data: Dict) -> List[str]:
    required_fields = ['title', 'author', 'functional_reqs', 'non_functional_reqs', 'capacity_estimation']
    errors = []
    for field in required_fields:
        if field not in input_data:
            errors.append(f'Missing required field: {field}')
    if 'rubric_items' not in input_data:
        input_data['rubric_items'] = 'Senior Engineering System Design Rubric 2026'
    return errors

def generate_doc(input_data: Dict) -> str:
    # Fill template with input data, use defaults for missing optional fields
    doc_data = {
        'title': input_data['title'],
        'author': input_data['author'],
        'date': datetime.now().strftime('%Y-%m-%d'),
        'rubric_items': input_data.get('rubric_items', 'Senior Engineering System Design Rubric 2026'),
        'functional_reqs': '\n'.join([f'- {req}' for req in input_data['functional_reqs']]),
        'non_functional_reqs': '\n'.join([f'- {req}' for req in input_data['non_functional_reqs']]),
        'capacity_estimation': input_data.get('capacity_estimation', 'To be filled'),
        'architecture_diagram': input_data.get('architecture_diagram', 'Insert Mermaid diagram here'),
        'component_breakdown': '\n'.join([f'- {comp}' for comp in input_data.get('component_breakdown', [])]),
        'db_schema': input_data.get('db_schema', 'To be filled'),
        'sharding_strategy': input_data.get('sharding_strategy', 'To be filled'),
        'load_balancing': '\n'.join([f'- {item}' for item in input_data.get('load_balancing', [])]),
        'caching_strategy': '\n'.join([f'- {item}' for item in input_data.get('caching_strategy', [])]),
        'fault_tolerance': '\n'.join([f'- {item}' for item in input_data.get('fault_tolerance', [])]),
        'tradeoffs': '\n'.join([f'- {item}' for item in input_data.get('tradeoffs', [])]),
        'monitoring': '\n'.join([f'- {item}' for item in input_data.get('monitoring', [])])
    }
    return DOC_TEMPLATE.format(**doc_data)

def save_doc(doc: str, output_path: str):
    try:
        with open(output_path, 'w', encoding='utf-8') as f:
            f.write(doc)
        # Also save as HTML for preview
        html = markdown.markdown(doc)
        with open(output_path.replace('.md', '.html'), 'w', encoding='utf-8') as f:
            f.write(f'{html}')
    except IOError as e:
        raise IOError(f'Error saving document: {e}')

def main():
    if len(sys.argv) != 3:
        print('Usage: python doc_generator.py  ', file=sys.stderr)
        sys.exit(1)
    input_path = sys.argv[1]
    output_path = sys.argv[2]
    try:
        input_data = load_input_file(input_path)
    except FileNotFoundError as e:
        print(e, file=sys.stderr)
        sys.exit(1)
    except ValueError as e:
        print(e, file=sys.stderr)
        sys.exit(1)

    errors = validate_input(input_data)
    if errors:
        print('Input validation errors:', file=sys.stderr)
        for error in errors:
            print(f'- {error}', file=sys.stderr)
        sys.exit(1)

    try:
        doc = generate_doc(input_data)
        save_doc(doc, output_path)
        print(f'Successfully generated system design doc at {output_path}')
        print(f'HTML preview saved at {output_path.replace(".md", ".html")}')
    except IOError as e:
        print(e, file=sys.stderr)
        sys.exit(1)

if __name__ == '__main__':
    main()

# Snippet: Rubric keyword check from Step 1 parser
def check_rubric_compliance(resume_text: str) -> Dict[str, List[str]]:
    compliance = {category: [] for category in RUBRIC_KEYWORDS}
    tokens = nltk.word_tokenize(resume_text)
    for category, keywords in RUBRIC_KEYWORDS.items():
        for keyword in keywords:
            if keyword in tokens or keyword in resume_text:
                compliance[category].append(keyword)
    return compliance

# Snippet: Tradeoff section template for system design docs
tradeoffs:
  - Chose Redis over Memcached for native hash support and persistence
  - Used LRU eviction policy to prioritize hot keys, at cost of cache miss for long-tail keys
  - Opted for eventual consistency for user profile data to reduce p99 write latency by 200ms

# Snippet: Open-source PR description template
title: "Add unit tests for Guava's ImmutableList"
description: "Fixes #1234 by adding 14 unit tests for edge cases in ImmutableList.subList(). Includes regression tests for JDK 17 compatibility."
rubric_alignment: "Open-source contribution, unit testing, edge cases"