Database Engineer

Learn SQL: the language for talking to databases. Used by literally every backend that stores anything. SELECT, JOIN, GROUP BY, aggregates, subqueries — once you have these you can answer any question your data can answer.

Level up from SQL Fundamentals. Subqueries, CTEs, window functions, set operations, transactions, indexes. The SQL features serious analytics and reporting use every day.

Beyond SQL Intermediate. Indexes, query optimization mental model, complex joins, JSON, advanced window functions, recursive CTEs. The features behind serious analytics and reporting.

Learn Rust: a systems language with the safety of a high-level one. Ownership, borrowing, pattern matching, zero-cost abstractions. Every lesson is hands-on with real cargo execution.

Level up from Rust Fundamentals. Ownership in depth, traits and trait objects, generics, lifetimes, iterator chains, Result/Option chaining, closures. The features that make Rust feel like Rust.

Beyond Rust Intermediate. Smart pointers (Box, Rc, RefCell), interior mutability, trait objects, declarative macros, unsafe basics. The features that close the gap between Rust and C++.

Learn C: the language behind operating systems, embedded firmware, and the foundation of nearly every modern language. Manual control, explicit memory, no surprises. Every lesson is hands-on with real gcc execution.

Build a production-grade key-value store from scratch. You'll start with an in-memory hash map, add ordered iteration and TTL, persist with write-ahead logging, build sorted SSTables, implement the LSM tree read path with compaction and Bloom filters, and add ACID transactions with MVCC snapshot isolation. By the end you'll deeply understand LevelDB, RocksDB, Cassandra, etcd, and FoundationDB.

Build a Log-Structured Merge tree like LevelDB/RocksDB: memtables, SSTables, leveled compaction, k-way merge, bloom filters, write-ahead logging, crash recovery, and the engine behind Cassandra, RocksDB, ScyllaDB, and modern time-series databases.

Build a fully functional relational database engine from the ground up. You'll implement a SQL parser, B-tree index, page-based storage, write-ahead log, ACID transactions, and a query planner — the same architecture used by SQLite, PostgreSQL, and MySQL. By the end, you'll understand exactly how every database you've ever used works internally.

Build a columnar OLAP engine like ClickHouse/Parquet: row vs column layout, RLE/dictionary/bit-packing encodings, vectorized execution, predicate pushdown, hash + sort-merge joins, and the storage layer behind Snowflake, BigQuery, and modern data warehouses.

Build a time-series database like Prometheus/InfluxDB: ingest streams of (timestamp, value, labels), Gorilla compression for 10x storage savings, time-partitioned blocks, downsampling pipelines, PromQL-style queries, histograms with t-digest percentiles, and HA via federation/Thanos. The metrics infrastructure of every modern observability stack.

Build a graph database like Neo4j/Memgraph: nodes + edges + properties storage, index-free adjacency for O(1) traversal, label/property indexes, Cypher-style query language, BFS/Dijkstra/PageRank algorithms, RDF triples and SPARQL alternative model, and the engine behind Twitter's social graph, fraud detection, and knowledge graphs.