Opinion: Why Staff Engineers Should Write Less Code and Negotiate More – Use Levels.fyi 2026 with Google and Meta

import requests
import json
import pandas as pd
from typing import List, Dict, Optional
import matplotlib.pyplot as plt

# Configuration: Levels.fyi public dataset v2026.03
DATASET_URL = "https://raw.githubusercontent.com/levelsdotfyi/public-datasets/main/2026/staff_engineer_surveys.json"
COMPANIES_OF_INTEREST = ["Google", "Meta"]
CODE_TIME_THRESHOLD = 15  # Percentage of time spent writing code

class StaffEngineerSurveyParser:
    def __init__(self, dataset_url: str):
        self.dataset_url = dataset_url
        self.raw_data: List[Dict] = []
        self.parsed_df: Optional[pd.DataFrame] = None

    def fetch_data(self) -> None:
        """Fetch raw survey data from GitHub-hosted dataset with error handling"""
        try:
            response = requests.get(self.dataset_url, timeout=10)
            response.raise_for_status()  # Raise HTTPError for bad responses (4xx, 5xx)
            self.raw_data = response.json()
            print(f"Fetched {len(self.raw_data)} survey responses")
        except requests.exceptions.Timeout:
            raise RuntimeError("Request to dataset timed out after 10 seconds")
        except requests.exceptions.HTTPError as e:
            raise RuntimeError(f"HTTP error fetching dataset: {e}")
        except json.JSONDecodeError:
            raise RuntimeError("Dataset is not valid JSON")

    def parse_to_dataframe(self) -> pd.DataFrame:
        """Parse raw JSON into structured DataFrame with validation"""
        if not self.raw_data:
            raise ValueError("No data fetched. Call fetch_data() first.")

        parsed_rows = []
        for entry in self.raw_data:
            # Validate required fields exist
            required_fields = ["company", "level", "total_compensation", "code_time_percentage"]
            if not all(field in entry for field in required_fields):
                continue  # Skip malformed entries

            # Filter for target companies and staff level
            if entry["company"] not in COMPANIES_OF_INTEREST:
                continue
            if entry["level"].lower() not in ["staff", "staff engineer", "l6", "level 6"]:  # Google L6, Meta L6 are staff
                continue

            parsed_rows.append({
                "company": entry["company"],
                "level": entry["level"],
                "tc": float(entry["total_compensation"]),
                "code_time": float(entry["code_time_percentage"]),
                "years_experience": int(entry.get("years_experience", 0))
            })

        self.parsed_df = pd.DataFrame(parsed_rows)
        print(f"Parsed {len(self.parsed_df)} valid staff engineer entries")
        return self.parsed_df

    def calculate_correlation(self) -> Dict:
        """Calculate correlation between code time and TC"""
        if self.parsed_df is None:
            self.parse_to_dataframe()

        low_code = self.parsed_df[self.parsed_df["code_time"] < CODE_TIME_THRESHOLD]
        high_code = self.parsed_df[self.parsed_df["code_time"] >= CODE_TIME_THRESHOLD]

        return {
            "low_code_avg_tc": low_code["tc"].mean(),
            "high_code_avg_tc": high_code["tc"].mean(),
            "tc_difference": low_code["tc"].mean() - high_code["tc"].mean(),
            "low_code_count": len(low_code),
            "high_code_count": len(high_code)
        }

if __name__ == "__main__":
    parser = StaffEngineerSurveyParser(DATASET_URL)
    try:
        parser.fetch_data()
        df = parser.parse_to_dataframe()
        stats = parser.calculate_correlation()

        print("\n=== 2026 Staff Engineer TC vs Code Time (Google/Meta) ===")
        print(f"Low code time (<{CODE_TIME_THRESHOLD}%): Avg TC ${stats['low_code_avg_tc']:,.2f} (n={stats['low_code_count']})")
        print(f"High code time (>={CODE_TIME_THRESHOLD}%): Avg TC ${stats['high_code_avg_tc']:,.2f} (n={stats['high_code_count']})")
        print(f"Difference: ${stats['tc_difference']:,.2f} in favor of low-code staff engineers")

        # Generate scatter plot
        plt.scatter(df["code_time"], df["tc"], alpha=0.6)
        plt.xlabel("Percentage of Time Writing Code")
        plt.ylabel("Total Compensation ($)")
        plt.title("Staff Engineer Code Time vs TC (Google/Meta 2026)")
        plt.savefig("staff_engineer_tc_correlation.png")
        print("Saved correlation plot to staff_engineer_tc_correlation.png")
    except RuntimeError as e:
        print(f"Error: {e}")
        exit(1)

import json
import os
from typing import Dict, List, Optional
from dataclasses import dataclass

# Path to local Levels.fyi 2026 staff TC bands (cached from public dataset)
TC_BANDS_PATH = os.path.join(os.path.dirname(__file__), "tc_bands_2026.json")
DEFAULT_BAND_MARGIN = 0.1  # Aim for 10% above median for initial offers

@dataclass
class NegotiationOffer:
    company: str
    level: str
    base_salary: float
    stock: float
    bonus: float
    total_compensation: float
    years_experience: int

    def __post_init__(self):
        if self.total_compensation != self.base_salary + self.stock + self.bonus:
            raise ValueError("Total compensation must equal sum of base, stock, and bonus")

class StaffNegotiationHelper:
    def __init__(self, tc_bands_path: str = TC_BANDS_PATH):
        self.tc_bands: Dict = {}
        self.load_tc_bands(tc_bands_path)

    def load_tc_bands(self, path: str) -> None:
        """Load cached TC band data with error handling"""
        try:
            with open(path, "r") as f:
                self.tc_bands = json.load(f)
            print(f"Loaded TC bands for {len(self.tc_bands.get('companies', []))} companies")
        except FileNotFoundError:
            raise RuntimeError(f"TC bands file not found at {path}. Run dataset fetch script first.")
        except json.JSONDecodeError:
            raise RuntimeError("TC bands file is corrupted or invalid JSON")

    def get_median_tc(self, company: str, level: str) -> Optional[float]:
        """Retrieve median TC for a given company and staff level"""
        company_bands = self.tc_bands.get("companies", {}).get(company, {})
        level_bands = company_bands.get(level, {})
        return level_bands.get("median_tc")

    def generate_talking_points(self, offer: NegotiationOffer) -> List[str]:
        """Generate data-backed talking points for negotiation"""
        median_tc = self.get_median_tc(offer.company, offer.level)
        if not median_tc:
            return ["No TC band data available for this company/level. Request data from recruiter."]

        points = []
        # Point 1: Compare to market median
        if offer.total_compensation < median_tc * (1 - DEFAULT_BAND_MARGIN):
            points.append(
                f"My current offer of ${offer.total_compensation:,.2f} is below the 2026 median TC of "
                f"${median_tc:,.2f} for {offer.level} at {offer.company}. "
                f"I'm requesting we align to at least the median."
            )
        elif offer.total_compensation < median_tc:
            points.append(
                f"My offer is slightly below the 2026 median TC of ${median_tc:,.2f} for {offer.level}. "
                f"I'd like to discuss adjusting the stock component to reach median."
            )

        # Point 2: Experience adjustment
        if offer.years_experience > 10:
            points.append(
                f"With {offer.years_experience} years of experience, including 4 years at staff level, "
                f"I bring proven ability to lead cross-team initiatives that reduce infra costs by 30%+. "
                f"This experience warrants compensation above the median band."
            )

        # Point 3: Competing offers
        points.append(
            "I have active interviews with two other FAANG companies at the same level, "
            "with expected offers in the $400k–$450k range. I'd prefer to join {company} "
            "but need to align compensation to make the decision easy.".format(company=offer.company)
        )

        return points

    def calculate_counter_offer(self, offer: NegotiationOffer, target_percentile: float = 0.75) -> Dict:
        """Calculate a counter offer at a given percentile of the TC band"""
        median_tc = self.get_median_tc(offer.company, offer.level)
        if not median_tc:
            return {"error": "No TC band data available"}

        # Simple model: target TC is median + (target_percentile - 0.5) * (p90 - p50)
        company_bands = self.tc_bands.get("companies", {}).get(offer.company, {})
        level_bands = company_bands.get(offer.level, {})
        p90 = level_bands.get("p90_tc", median_tc * 1.2)
        target_tc = median_tc + (target_percentile - 0.5) * (p90 - median_tc)

        # Split adjustment to stock (most negotiable component)
        adjustment = target_tc - offer.total_compensation
        new_stock = offer.stock + adjustment * 0.8  # 80% of adjustment to stock
        new_bonus = offer.bonus + adjustment * 0.2  # 20% to bonus

        return {
            "original_tc": offer.total_compensation,
            "target_tc": round(target_tc, 2),
            "counter_offer": {
                "base_salary": offer.base_salary,
                "stock": round(new_stock, 2),
                "bonus": round(new_bonus, 2),
                "total_compensation": round(new_stock + new_bonus + offer.base_salary, 2)
            },
            "talking_points": self.generate_talking_points(offer)
        }

if __name__ == "__main__":
    helper = StaffNegotiationHelper()
    # Example offer: Google L6 staff engineer, 12 years experience
    sample_offer = NegotiationOffer(
        company="Google",
        level="L6",
        base_salary=180000,
        stock=150000,
        bonus=50000,
        total_compensation=380000,
        years_experience=12
    )

    try:
        counter = helper.calculate_counter_offer(sample_offer, target_percentile=0.75)
        print("\n=== Negotiation Counter Offer (Google L6) ===")
        print(f"Original Offer: ${counter['original_tc']:,.2f}")
        print(f"Target TC (75th percentile): ${counter['target_tc']:,.2f}")
        print("\nCounter Offer Breakdown:")
        for key, val in counter["counter_offer"].items():
            print(f"  {key}: ${val:,.2f}")
        print("\nTalking Points:")
        for i, point in enumerate(counter["talking_points"], 1):
            print(f"{i}. {point}")
    except RuntimeError as e:
        print(f"Error: {e}")
        exit(1)

import sqlite3
import datetime
from typing import List, Dict, Optional
from dataclasses import dataclass

DATABASE_PATH = "negotiation_tracker.db"

@dataclass
class NegotiationRecord:
    company: str
    level: str
    original_tc: float
    final_tc: float
    negotiation_rounds: int
    success: bool
    notes: str
    date: str = datetime.datetime.now().strftime("%Y-%m-%d")

class NegotiationTracker:
    def __init__(self, db_path: str = DATABASE_PATH):
        self.db_path = db_path
        self.init_database()

    def init_database(self) -> None:
        """Initialize SQLite database with error handling"""
        try:
            conn = sqlite3.connect(self.db_path)
            cursor = conn.cursor()
            cursor.execute("""
                CREATE TABLE IF NOT EXISTS negotiations (
                    id INTEGER PRIMARY KEY AUTOINCREMENT,
                    company TEXT NOT NULL,
                    level TEXT NOT NULL,
                    original_tc REAL NOT NULL,
                    final_tc REAL NOT NULL,
                    negotiation_rounds INTEGER NOT NULL,
                    success INTEGER NOT NULL,  -- 0 for false, 1 for true
                    notes TEXT,
                    date TEXT NOT NULL
                )
            """)
            conn.commit()
            conn.close()
            print(f"Initialized negotiation tracker database at {self.db_path}")
        except sqlite3.Error as e:
            raise RuntimeError(f"Database initialization failed: {e}")

    def add_record(self, record: NegotiationRecord) -> int:
        """Add a new negotiation record to the database"""
        try:
            conn = sqlite3.connect(self.db_path)
            cursor = conn.cursor()
            cursor.execute("""
                INSERT INTO negotiations (company, level, original_tc, final_tc, negotiation_rounds, success, notes, date)
                VALUES (?, ?, ?, ?, ?, ?, ?, ?)
            """, (
                record.company,
                record.level,
                record.original_tc,
                record.final_tc,
                record.negotiation_rounds,
                1 if record.success else 0,
                record.notes,
                record.date
            ))
            conn.commit()
            record_id = cursor.lastrowid
            conn.close()
            print(f"Added negotiation record {record_id} for {record.company} {record.level}")
            return record_id
        except sqlite3.Error as e:
            raise RuntimeError(f"Failed to add record: {e}")

    def get_success_rate(self, company: Optional[str] = None, level: Optional[str] = None) -> float:
        """Calculate negotiation success rate, optionally filtered by company/level"""
        try:
            conn = sqlite3.connect(self.db_path)
            cursor = conn.cursor()

            query = "SELECT success FROM negotiations WHERE 1=1"
            params = []
            if company:
                query += " AND company = ?"
                params.append(company)
            if level:
                query += " AND level = ?"
                params.append(level)

            cursor.execute(query, params)
            rows = cursor.fetchall()
            conn.close()

            if not rows:
                return 0.0
            success_count = sum(1 for row in rows if row[0] == 1)
            return (success_count / len(rows)) * 100
        except sqlite3.Error as e:
            raise RuntimeError(f"Failed to calculate success rate: {e}")

    def get_average_tc_increase(self, company: Optional[str] = None) -> float:
        """Calculate average TC increase from negotiation"""
        try:
            conn = sqlite3.connect(self.db_path)
            cursor = conn.cursor()

            query = "SELECT original_tc, final_tc FROM negotiations WHERE success = 1"
            params = []
            if company:
                query += " AND company = ?"
                params.append(company)

            cursor.execute(query, params)
            rows = cursor.fetchall()
            conn.close()

            if not rows:
                return 0.0
            total_increase = sum(final - original for original, final in rows)
            return total_increase / len(rows)
        except sqlite3.Error as e:
            raise RuntimeError(f"Failed to calculate average increase: {e}")

if __name__ == "__main__":
    tracker = NegotiationTracker()
    # Add sample successful negotiation
    sample_record = NegotiationRecord(
        company="Meta",
        level="L6",
        original_tc=350000,
        final_tc=420000,
        negotiation_rounds=2,
        success=True,
        notes="Negotiated stock increase after sharing Levels.fyi 2026 data"
    )

    try:
        tracker.add_record(sample_record)
        print("\n=== Negotiation Tracker Stats ===")
        print(f"Overall success rate: {tracker.get_success_rate():.1f}%")
        print(f"Meta success rate: {tracker.get_success_rate(company='Meta'):.1f}%")
        print(f"Average TC increase (successful): ${tracker.get_average_tc_increase():,.2f}")
        print(f"Average Meta TC increase: ${tracker.get_average_tc_increase(company='Meta'):,.2f}")
    except RuntimeError as e:
        print(f"Error: {e}")
        exit(1)

import pandas as pd
# Load your last 4 weeks of time tracking data
time_data = pd.read_csv("my_time_tracking.csv")
code_hours = time_data[time_data["category"] == "coding"]["hours"].sum()
total_hours = time_data["hours"].sum()
code_ratio = (code_hours / total_hours) * 100
print(f"Code time ratio: {code_ratio:.1f}% (target: <15%)")

import json
# Load peer team benchmark data from Levels.fyi
with open("peer_team_benchmarks.json") as f:
    benchmarks = json.load(f)
my_team_size = 4  # seniors
peer_avg_size = benchmarks["Google"]["L6"]["avg_team_size"]
print(f"My team size: {my_team_size}, Peer average: {peer_avg_size:.1f}")
print(f"Request {max(0, round(peer_avg_size - my_team_size))} additional headcount")

import re
# Check promotion packet for code-related keywords
packet_text = open("promotion_packet.md").read()
code_keywords = ["lines of code", "PRs merged", "commits", "code reviews"]
for keyword in code_keywords:
    count = len(re.findall(keyword, packet_text, re.IGNORECASE))
    if count > 0:
        print(f"Remove {count} mentions of '{keyword}' from promotion packet")