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Engineering: backend-engineering, frontend-engineering, data-engineering, ml-engineering, platform-engineering, qa-methodology Executive: go-to-market, legal-strategy, operational-design, org-design, product-strategy ml-engineering: added missing training-infrastructure.md reference qa-methodology: added test-data-management, performance-testing, security-testing references All frontmatter converted to agent-skills convention. Source: https://github.com/magnus919/hermes-profiles
56 lines
4.8 KiB
Markdown
56 lines
4.8 KiB
Markdown
---
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name: data-engineering
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description: Data engineering methodology — database operations (vector, relational,
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graph, time-series), ETL/ELT pipeline design (dbt patterns, incremental loading),
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SQL analytical patterns, data quality monitoring, schema migration, and storage
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infrastructure management. Grounded in operational patterns for production data
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systems.
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license: MIT
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metadata:
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tags: data-engineering, etl, dbt, sql, database, graph-db, time-series, vector-db,
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migration, data-quality, storage, influxdb, neo4j
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source_repo: https://github.com/magnus919/hermes-profiles
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---
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# Data Engineering Methodology
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Data engineering is the operational backbone of data-driven systems. This methodology covers running, maintaining, and evolving data infrastructure — from relational databases and vector stores to graph databases, time-series stores, and the transformation pipelines that move data between them.
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## The Data Engineer's Domain
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| You own | You don't own |
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|---------|--------------|
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| Database operations — schema management, indexing, backup/recovery, migration across relational, vector, graph, and time-series stores | Data modeling and schema design — that's the data architect |
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| Data transformation pipelines — dbt models, ETL/ELT patterns, incremental loading, incremental strategies | Statistical analysis and experiments — that's the data scientist |
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| Analytical SQL — window functions, CTEs, query optimization, execution plan analysis, star schema queries | Training infrastructure and model deployment — that's the ML engineer |
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| Graph database operations — Neo4j data modeling, Cypher queries, graph algorithms, import/export | Application-level data access patterns — that's the developer |
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| Time-series database operations — InfluxDB schema design, downsampling, retention policies, Telegraf | Infrastructure provisioning — that's the platform engineer |
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| Data quality monitoring — integrity checks, deduplication, anomaly detection, freshness validation | Visual dashboard design — that's the analyst or UX designer |
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| Storage infrastructure — capacity planning, performance tuning, archival strategies |
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## Reference Files
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| Reference | When to load |
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|-----------|-------------|
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| `references/sql-analytical-patterns.md` | Writing analytical SQL — window functions, CTEs, execution plan reading, star schema queries, engine-specific optimization (PostgreSQL, DuckDB, ClickHouse, BigQuery, Snowflake) |
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| `references/dbt-patterns.md` | Designing data transformation pipelines with dbt — project structure, modeling layers (staging/intermediate/facts/dimensions), materializations, tests, snapshots, Jinja macros, CI/CD, dbt Mesh |
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| `references/etl-pipeline-design.md` | Building reliable data pipelines — extraction strategies (full, incremental, CDC), transformation layers, validation gates, error handling, idempotency |
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| `references/data-quality.md` | Monitoring data integrity — quality dimensions, validation rule types, anomaly detection, deduplication strategies, pipeline health signals |
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| `references/graph-databases.md` | Working with graph databases — Neo4j data modeling, Cypher query patterns (traversal, aggregation, pathfinding), import strategies, graph algorithms, pipeline integration |
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| `references/time-series-databases.md` | Working with time-series databases — InfluxDB data model (measurements, tags, fields), schema design (cardinality), downsampling, retention, Telegraf ingest, comparison with TimescaleDB/QuestDB/Prometheus |
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| `references/vector-db-operations.md` | Managing vector databases — Milvus, Qdrant, Chroma — index types, collection lifecycle, dimension migrations, backup strategies |
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| `references/database-migrations.md` | Schema evolution — zero-downtime migration patterns, rollback planning, versioned schemas, test-first migrations |
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| `references/backup-and-recovery.md` | Backup strategies per data store type, RPO/RTO planning, WAL archiving, snapshot management, recovery plan template |
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## Core Principles
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**Data without integrity is noise** — No pipeline, model, or dashboard is worth more than the quality of the data feeding it. Validate at every boundary.
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**Design for operability** — Every database, pipeline, and store needs monitoring, backup, and recovery procedures defined before it goes to production. If you can't detect failure, you can't recover from it.
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**Idempotency is a requirement** — Every pipeline should produce the same result whether it runs once or twice. Duplicate handling is not optional.
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**Schema changes are code changes** — Every migration needs review, testing, and a rollback plan. Schema drift is technical debt with compounding interest.
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**Know your storage characteristics** — Access patterns, retention requirements, growth rates, and consistency guarantees determine the right storage architecture. Choose based on data, not familiarity.
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