Data Engineering
The modern data stack from a senior analytics-engineering angle — SQL, dbt, modeling, warehouses, orchestration, observability.
12 guides·147 chapters·~20 h of reading
No.GuideFormatLengthRead
Interview prep6
- 01Data Analytics EngineeringInterview prep for Senior Data Analytics Engineer / Data Engineer roles, tilted toward modern AI / GPU / inference platform companies. 18 pages covering SQL, dbt, modeling, warehouses, orchestration, and observability, plus a 30-question drill set and an AI-compute domain chapter.Interview prep18 ch.~2 h
- 02Data Systems DesignPrep for the 60-minute, discussion-based data systems design interview — architecting scalable, reliable data platforms and defending the big trade-offs. A repeatable design framework (requirements → architecture → trade-offs → deep dives → evolution), then the building blocks (requirements & sizing, ingestion, CDC strategies) and the decisions the interview names by name: batch vs streaming, data lake vs warehouse vs lakehouse, and build vs buy — plus scalability/reliability and governance. 13 chapters, a full whiteboard design walkthrough, and a ~30-question drill set. Companion to Production Data Pipelines.Interview prep13 ch.~2 h
- 03First Data Hire — DE Interview PrepInterview prep for a Data Engineer role where you'd be the company's first data hire. The three graded skills — SQL, data quality & pipelines, and modeling judgement — each with a foundations chapter and a deep advanced chapter, read through the founding-hire lens of sequencing, trust, and build-vs-buy. 15 chapters, three drill sets (24 SQL problems + ~35 Q&A), a usage / GPU-compute marketplace domain chapter, and a defensible 30/60/90 plan.Interview prep15 ch.~2 h
- 04Forward Deployed Engineer — Data EngineeringInterview prep for FDE-flavored Data Engineering roles at enterprise contract-intelligence / document-AI platforms. The Palantir-originated FDE archetype applied to embedded customer deployments — ingestion pipelines for messy contract/invoice/PO corpora, SLA-backed extraction quality, customer warehouse and ERP integration, and the consultant + engineer duality the role tests.Interview prep17 ch.~2 h
- 05Production Data PipelinesPrep for the 60-minute, discussion-based production-pipeline interview — the deep-dive on building robust, real-world data systems. Pipeline design & orchestration, late-arriving data, incremental processing, reconciliation & validation, reliability/observability, and scale — each built as a progression from foundational to staff-level so you can level up your answers on demand. Tool-agnostic (patterns first) with dbt and Airflow as worked examples. 12 chapters, a whiteboard-driven design walkthrough, and a ~30-question drill set.Interview prep12 ch.~2 h
- 06Senior Data Analytics EngineerDrill-focused prep for a founding-analytics screen — SQL creation, SQL debugging, and Python pandas LeetCode-style problems. 11 chapters with two dedicated drill sets and a GPU compute / AI infrastructure domain chapter.Interview prep11 ch.~1 h
Build-along1
Deep dive2
- 08Data Engineering for NeocloudsTwo sections in one guide. Section A: how data engineers run training-data pipelines, orchestration, and cost engineering on neocloud infrastructure (Vast / CoreWeave / Together / RunPod / etc.). Section B: how DE teams *inside* neoclouds build the telemetry, billing, marketplace event streams, and capacity-planning data that run the platform.Deep dive12 ch.~2 h
- 09Document AI Extraction — Deep DiveA working reference on the document-extraction stack — OCR, layout-aware models (LayoutLM family, DocFormer, LiLT), field-extraction patterns, confidence calibration, human-in-the-loop economics, evaluation methodology, drift detection. For practitioners who need to design or debug a production extraction system.Deep dive11 ch.~1 h
Reference2
- 10Contract Data Model — ReferenceThe contract / agreement / spend data model with rationale for every decision — supplier resolution, document-vs-agreement split, obligation versioning, temporal modeling. Plus 30+ SQL query patterns with discussion and schema-evolution recipes.Reference10 ch.~1 h
- 11Enterprise Integration AtlasA reference atlas for the enterprise systems FDE-DEs meet on every deployment — SAP (ECC / S/4HANA), Oracle, Workday, NetSuite, Dynamics 365, Coupa, Ariba, Snowflake, BigQuery, Databricks, Looker, Power BI, Tableau, Hex, Okta, Entra. API shapes, auth patterns, gotchas, code snippets.Reference10 ch.~1 h
Playbook1