Blockchain-Specific Skills

DeFi Mechanics

Understanding how DeFi protocols work mechanically — not just conceptually — is what separates junior contributors from senior ones. When you know why a lending protocol's interest rate spikes at 90% utilisation, you can anticipate user behaviour, predict stress scenarios, and design better systems.

Automated Market Makers (AMMs)

The constant product formula x × y = k underpins Uniswap V2 and most basic AMMs. When you trade token X for token Y, you increase X and decrease Y in the pool, shifting the price along a hyperbolic curve. The larger your trade relative to pool size, the more price slippage you experience — this is a mathematical property of the curve, not a bug.

Uniswap V3 introduced concentrated liquidity: instead of spreading capital uniformly across all prices from zero to infinity, liquidity providers specify a price range where their capital is active. This dramatically increases capital efficiency (up to 4000x in tight ranges) but amplifies impermanent loss risk when prices move outside your range. Understanding this trade-off is essential for evaluating LP positions and designing vault strategies.

Curve Finance optimised for stablecoin swaps with a StableSwap invariant that sits between constant product and constant sum, enabling lower slippage between similarly-priced assets. Balancer generalised further: customisable multi-token pools with arbitrary weight allocations. Each variant creates different trade-offs you must understand to evaluate protocol competitive positioning.

Lending Protocol Mechanics

DeFi lending protocols like Aave and Compound require overcollateralisation — borrowers deposit more value than they borrow — because there's no identity system or legal recourse in a permissionless environment. If your collateral value drops below the liquidation threshold (typically 75-85% LTV), liquidators automatically repay your debt and claim your collateral at a discount. This mechanism maintains protocol solvency but creates risk of cascading liquidations during volatile markets.

Interest rate models use utilisation curves: borrow rates are low when little capital is borrowed and spike sharply as utilisation approaches 100%, incentivising suppliers to deposit more and borrowers to repay. The “kink” in most rate curves — where the slope increases dramatically — typically sits at 80-90% utilisation. Understanding this helps you model protocol revenue, assess user incentives, and identify stress scenarios.

Yield Optimisation

Yield aggregators like Yearn Finance and Convex automate the strategy of moving capital between protocols to maximise returns. Understanding yield strategies means grasping how compounding frequencies, gas costs, and protocol incentives interact. A strategy earning 15% APY that compounds daily incurs gas on every compounding transaction — on Ethereum mainnet this can erase gains for small positions. Understanding break-even thresholds is a practical analytical skill.

Token Economics

Token economics — tokenomics — is the study of how token supply, distribution, incentives, and value accrual mechanisms interact to shape protocol behaviour. A protocol with excellent technology and poor tokenomics will fail. A protocol with mediocre technology and well-designed tokenomics can succeed. The ability to evaluate tokenomics is a high-value skill that very few candidates possess.

Supply dynamics determine how tokens enter and leave circulation. Inflationary tokens mint new supply continuously to reward participants — this can incentivise early participation but dilutes existing holders. Deflationary mechanisms like fee burns (EIP-1559 burns ETH with every transaction) reduce circulating supply. The interaction between emissions and burns creates dynamic supply schedules you should be able to model across different usage scenarios.

Vesting schedules for team, investor, and foundation allocations matter enormously for understanding token supply pressure. A protocol launching with 40% team allocation and a 12-month cliff followed by 24-month linear vesting will experience significant sell pressure at months 12-36. Reading tokenomics documentation and cross-referencing on-chain vesting contracts is standard due diligence.

Protocol-Owned Liquidity (POL) and ve-token models represent innovations in protocol-level token design. Curve's veCRV model rewards long-term token lockers with boosted yields and governance power, aligning incentives between holders and the protocol. Protocols owning their own liquidity rather than renting it from mercenary LPs creates more stable, sustainable liquidity — this shift, pioneered by OlympusDAO and adopted broadly, changes how you evaluate protocol sustainability.

Cryptography Fundamentals

You don't need to implement cryptographic primitives to work effectively in blockchain — but you absolutely must understand what they enable and where they can fail. This section covers the practical knowledge required, not the mathematics.

Public/private key cryptography underlies every blockchain identity and transaction. Your private key is a 256-bit random number; your public key is derived from it through elliptic curve multiplication (specifically the secp256k1 curve for Bitcoin and Ethereum). Your address is the last 20 bytes of the Keccak-256 hash of your public key. The security assumption: deriving a private key from a public key is computationally infeasible. If this assumption breaks — for instance, with sufficiently powerful quantum computers — the entire system collapses.

Hash functions (SHA-256 in Bitcoin, Keccak-256 in Ethereum) convert arbitrary data into fixed-length outputs deterministically. The same input always produces the same output; changing even one bit produces a completely different hash. This property powers transaction IDs, block linking, and Merkle trees. A Merkle tree enables efficient verification that a transaction is included in a block without downloading the entire block — critical for lightweight clients and rollup proof systems.

Digital signatures prove that a transaction was authorised by the private key holder without revealing the private key. Ethereum uses ECDSA (Elliptic Curve Digital Signature Algorithm). Every transaction carries a signature that validators verify before including it in a block. EIP-712 standardised structured data signing, enabling dApps to present human-readable signing requests instead of opaque hex strings.

Zero-Knowledge Proofs (ZKPs) allow one party to prove they know something without revealing what they know. Applied to blockchain: a ZK-Rollup generates a cryptographic proof that hundreds of transactions were executed correctly, submitting only the proof to Ethereum — not the transactions themselves. This compresses state dramatically and enables massive throughput improvements.

The key distinction: ZK-SNARKs (Succinct Non-interactive Arguments of Knowledge) produce tiny proofs but require a trusted setup ceremony — a one-time process where if participants collude, the entire system can be compromised. ZK-STARKs (Scalable Transparent Arguments of Knowledge) require no trusted setup and are quantum-resistant, but produce larger proofs. zkSync uses SNARKs; StarkNet uses STARKs. Understanding this trade-off matters when evaluating which ZK ecosystem to build on or analyse.

On-Chain Analytics

Every transaction on a public blockchain is permanently recorded and publicly visible. This creates an unprecedented dataset for analysis — and the ability to extract signal from it is a genuinely differentiated skill that employers value.

Reading block explorers is the baseline. Etherscan (Ethereum), Solscan (Solana), and their equivalents let you inspect any transaction, wallet, or smart contract. For professionals, the critical skills are: reading contract ABIs to understand what functions exist, tracing the internal calls of a complex DeFi transaction, interpreting event logs to understand what a contract emitted, and identifying contract deployers and upgrade history. These skills are essential for due diligence, security review, and competitive analysis.

Wallet tracking involves monitoring known addresses — protocol treasuries, large holders (“whales”), smart money wallets, and institutional addresses — to detect patterns. Nansen labels millions of wallets based on transaction patterns and self-reported identity. Tracking treasury movements can reveal protocol decisions before announcements; tracking smart money inflows to a protocol is an early signal of growing interest.

MEV (Maximal Extractable Value) is the value that can be extracted from users by manipulating transaction ordering within a block. MEV bots sandwich trades — inserting a buy before a large trade and a sell after, profiting from the price impact. Flashbots introduced structured MEV markets that reduced chaotic gas wars; mev-boost in Ethereum's proof-of-stake architecture enables validators to outsource block building to specialised builders. Understanding MEV is essential for protocol design (design your contracts to be MEV-resistant), user experience (warn users about sandwich risk), and career positioning in a growing specialisation.