Data Engineering

Interview 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.

Drill-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 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.

A 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.

Hands-on build-along — ingest sample contracts, run OCR + open-source LLM extraction, persist to Postgres against a real contract data model, build an eval harness, expose results via API. Working code at every step.

The practitioner's operating manual once you're in role. 90-day plan, weekly cadence templates, working-agreement template, discovery question banks, kill-decision worksheets, runbook templates, handoff checklists. Reusable artifacts for embedded data-engineering deployments.

The 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.

A 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.

Two 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.

Interview 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.