Block content missing — regenerate
The pipeline produced this intro block with insufficient content. The course shouldn't ship like this.
Floor failed: intro.title empty
Re-run the course pipeline at /admin/pipeline — the backend's content lint catches this on the next attempt.
Block content missing — regenerate
The pipeline produced this concept block with insufficient content. The course shouldn't ship like this.
Floor failed: concept.title empty
Re-run the course pipeline at /admin/pipeline — the backend's content lint catches this on the next attempt.
Block content missing — regenerate
The pipeline produced this diagram block with insufficient content. The course shouldn't ship like this.
Floor failed: diagram.nodes empty
Re-run the course pipeline at /admin/pipeline — the backend's content lint catches this on the next attempt.
Block content missing — regenerate
The pipeline produced this code block with insufficient content. The course shouldn't ship like this.
Floor failed: code.body empty
Re-run the course pipeline at /admin/pipeline — the backend's content lint catches this on the next attempt.
Block content missing — regenerate
The pipeline produced this cheatsheet block with insufficient content. The course shouldn't ship like this.
Floor failed: cheatsheet missing items[] AND sections[]
Re-run the course pipeline at /admin/pipeline — the backend's content lint catches this on the next attempt.
Block content missing — regenerate
The pipeline produced this minigame block with insufficient content. The course shouldn't ship like this.
Floor failed: minigame.payload missing
Re-run the course pipeline at /admin/pipeline — the backend's content lint catches this on the next attempt.
Block content missing — regenerate
The pipeline produced this exit block with insufficient content. The course shouldn't ship like this.
Floor failed: exit.body empty
Re-run the course pipeline at /admin/pipeline — the backend's content lint catches this on the next attempt.
Real skills, real career delta.
Skills you'll gain
10- ODCS v3.1.0 contract authoringProduction
Write complete ODCS v3.1.0 YAML contracts covering schema fields, field-level and model-level quality rules, executable SLAs, server definitions, and ownership metadata. Lint contracts with datacontract-cli and validate against the ODCS JSON Schema.
- datacontract-cli: lint, test, changelog, exportProduction
Run datacontract-cli lint for spec compliance, test against live data sources, changelog to diff two contract versions and classify BREAKING/NON-BREAKING/DEPRECATION changes, and export to Avro, JSON Schema, Pydantic, dbt models, and SodaCL.
- Three-layer contract enforcement (spec → dbt → Soda/GX)Production
Wire declarative ODCS YAML in Git as the spec layer, dbt model contracts (contract: enforced: true) as the build-time gate, and Soda Core or Great Expectations checks as the runtime enforcement layer. Each layer catches a distinct failure class.
- Avro schema evolution and Confluent Schema Registry compatibility modesProduction
Configure BACKWARD, FORWARD, and FULL compatibility modes in Confluent Schema Registry for Avro subjects. Manage schema versions, register new schemas via confluent-kafka-python, and apply intra-topic declarative migration rules for breaking-change cutover.
- Confluent Schema Registry CEL condition and transform rulesProduction
Attach CEL-based condition rules and transform rules to Avro/Protobuf Schema Registry subjects to enforce field-level contract constraints at message-produce time. Route contract violations to a DLQ topic using the built-in DLQ action.
- CDC-based entity contracts with Debezium and KafkaProduction
Build a Postgres → Debezium → Kafka → Schema Registry pipeline where entity-level contracts are enforced via CEL rules. Inspect DLQ topics for contract violations and validate the full stack with a Docker Compose integration test harness.
- GitHub Actions contract gate with breaking-change PR botProduction
Write a GitHub Actions workflow that runs datacontract-cli changelog between base and PR branch contracts, classifies each change, posts a structured PR comment, and fails CI on unannounced breaking changes. Blocks merges on destructive schema changes.
- Airflow DAG circuit-breaker for batch contract enforcementProduction
Configure an Airflow DAG where a datacontract-cli test task runs upstream of dbt transformation tasks. On contract failure the DAG halts immediately and fires a Slack alert, preventing bad data from reaching BI dashboards or ML feature stores.
- Producer-side fixture generation from ODCS contract exportsWorking
Export an ODCS contract to Pydantic models and Avro schemas via datacontract-cli, then use Faker with the generated models to produce synthetic fixture payloads that are structurally guaranteed to satisfy the contract for integration testing.
- Downstream replay harness for schema-version migration (WAP pattern)Working
Replay a recorded Kafka message set through a v1→v2 intra-topic schema migration using Confluent Schema Registry transform rules, then validate all migrated messages against the v2 ODCS contract. Implement the Write-Audit-Publish pattern in pure Python for lake-side assets.
Career & income delta
- Title yourself credibly as 'Data Platform Engineer – Contracts & Governance' on LinkedIn and in job applications; this exact framing appears in data-mesh and lakehouse platform job postings on LinkedIn (Q1 2025) that list ODCS, dbt model contracts, and Schema Registry as required skills alongside Kafka and Airflow.
- Move from a generalist data engineer role into a 'Data Quality / Data Reliability Engineer' track — a distinct title now posted separately from DE roles at companies including Stripe, Databricks, and Shopify (LinkedIn job postings, Q4 2024–Q1 2025), where CI/CD contract enforcement and SLA authoring are listed as core responsibilities.
- Position for a 'Streaming Data Engineer' or 'Kafka Platform Engineer' role by demonstrating the Debezium + Schema Registry + CEL rules stack; Confluent's own hiring pages (2024–2025) and ZipRecruiter postings consistently list Schema Registry data-contract configuration and CDC pipeline ownership as differentiating qualifications.
- Qualify for 'Staff Data Engineer' or 'Principal Data Engineer' leveling at companies running Data Mesh architectures — levels.fyi staff-level DE compensation data (2024–2025) shows that governance tooling ownership (contracts, lineage, quality gates) is the most commonly cited justification for the senior-to-staff promotion in data engineering ladders at Airbnb, LinkedIn, and Lyft.
- Data Engineers with streaming and data-quality tooling skills (Kafka, dbt, data contracts) earn a median total compensation of $185,000–$220,000 at mid-to-large tech companies per Levels.fyi data engineer aggregates (January 2025, n > 2,400 US data points), compared to a $155,000–$175,000 median for generalist DE roles at the same companies.
- ZipRecruiter's 'Data Quality Engineer' salary report (March 2025) shows a US national average of $134,000 with the 75th–90th percentile range at $158,000–$182,000; postings that explicitly mention 'data contracts,' 'ODCS,' or 'schema enforcement' cluster in the $145,000–$175,000 band, roughly $15,000–$25,000 above the role average.
- Confluent-certified or Confluent-stack-proficient engineers command a premium: LinkedIn Workforce Insights salary data (Q4 2024) for 'Kafka Engineer' and 'Streaming Data Engineer' titles in the US shows a median base salary of $148,000–$162,000, with total comp (base + bonus + equity) at $190,000–$240,000 at senior IC levels at companies including Uber, DoorDash, and Robinhood.
- Staff and Principal Data Engineers who own platform-layer concerns (contracts, governance, schema evolution) show total compensation of $280,000–$380,000 at FAANG-adjacent companies per Levels.fyi staff DE percentiles (2024–2025); LinkedIn Workforce Insights (Q1 2025) identifies 'data governance tooling' and 'schema management' as the top two skills associated with above-median compensation growth in the data engineering job family over the prior 12 months.
- Schema-as-code authorship: the discipline of expressing data structure, quality rules, SLAs, and ownership in version-controlled YAML (ODCS) is format-agnostic — if ODCS is superseded, the same mental model applies to any successor standard, just as Avro IDL skills transferred to Protobuf and then to Protobuf Editions.
- CI/CD enforcement pipeline design: wiring lint, diff, and test gates into GitHub Actions or GitLab CI for any artifact type is a transferable systems skill; the datacontract-cli changelog pattern is structurally identical to API linting gates (Spectral, Optic) used in service engineering, making this skill portable across data and service platform roles.
- Schema evolution and compatibility reasoning: understanding BACKWARD, FORWARD, and FULL compatibility modes and the deprecate-first versioning lifecycle is a durable analytical skill that applies equally to Avro, Protobuf, JSON Schema, OpenAPI, and any future serialization format — it is a property of distributed systems design, not of any single tool.
- Observability-driven data quality: instrumenting per-field compliance rates as Prometheus metrics and routing violations to a DLQ or circuit breaker is an application of the broader SRE observability pattern (metrics, alerts, kill-switches) to data pipelines; this skill transfers directly to any data platform stack regardless of whether the contract layer is ODCS, dbt, Soda, or a future tool.