{
  "version": "1",
  "data": {
    "services": [
      {
        "slug": "cloud-data-platform-design",
        "name": "Cloud data platform design",
        "problemsSolved": "Greenfield data platform without a clear architectural backbone, or an early platform that already shows scaling and governance debt.",
        "situation": "Startup or scale-up with growing data needs, no dedicated platform team yet, leadership wants a design that survives the next 18 months without a rewrite.",
        "technologies": [
          "AWS",
          "Azure",
          "Terraform",
          "CDKTF",
          "Databricks",
          "Delta Lake",
          "Python"
        ],
        "outcome": "Documented architecture with IaC scaffold, dev/prod parity, IAM and cost-allocation model. Ready for a small team to build on without re-litigating fundamentals."
      },
      {
        "slug": "lakehouse-architecture",
        "name": "Lakehouse architecture",
        "problemsSolved": "Mixed warehouse + lake setups with unclear ownership of source-of-truth tables; teams duplicating ingestion because the lake is unreliable.",
        "situation": "Mid-size data team running Snowflake or Redshift alongside ad-hoc S3 / ADLS storage, wants a single Medallion-style platform with governed semantics.",
        "technologies": [
          "Databricks",
          "Unity Catalog",
          "Delta Lake",
          "Iceberg",
          "Spark",
          "dbt",
          "Terraform"
        ],
        "outcome": "Bronze / Silver / Gold layers with explicit contracts. Single catalog, governed access, reproducible builds. Downstream teams stop maintaining shadow copies."
      },
      {
        "slug": "spark-databricks-migration",
        "name": "Spark / Databricks migration",
        "problemsSolved": "Open-source Spark on EMR / HDInsight / self-managed clusters costing more in ops than the workloads justify, or stuck on legacy runtime versions.",
        "situation": "Existing Spark codebase, often EMR-based, with brittle cluster lifecycle and a team that does not want to keep babysitting it.",
        "technologies": [
          "Spark",
          "Databricks",
          "PySpark",
          "Delta Lake",
          "AWS",
          "Azure",
          "Airflow"
        ],
        "outcome": "Workloads migrated to Databricks (Unity-governed). Operational overhead drops, runtime upgrades become a one-click action, cost per job is measured instead of guessed."
      },
      {
        "slug": "legacy-etl-modernisation",
        "name": "Legacy ETL modernisation",
        "problemsSolved": "On-prem Informatica / SSIS / SAS-style ETL that nobody wants to touch; replays break things, late data corrupts marts, timezone handling is folklore.",
        "situation": "Enterprise data team with working but fragile pipelines, business stakeholders losing trust, modernisation already attempted once and stalled.",
        "technologies": [
          "Spark",
          "dbt",
          "Airflow",
          "Python",
          "Databricks",
          "AWS Glue",
          "DuckDB"
        ],
        "outcome": "Idempotent, config-driven pipelines. Replays are safe, late data is handled explicitly, schema changes are versioned. The on-call rotation gets quieter."
      },
      {
        "slug": "aws-azure-platform-implementation",
        "name": "AWS / Azure platform implementation",
        "problemsSolved": "Cloud account set up by click-ops, no clear environment separation, IAM grew organically, drift between dev and prod.",
        "situation": "Team needs production-grade AWS or Azure foundations under a data or AI workload, but has no platform engineer.",
        "technologies": [
          "AWS",
          "Azure",
          "Terraform",
          "CDK",
          "CDKTF",
          "GitHub Actions",
          "OIDC",
          "Key Vault",
          "IAM"
        ],
        "outcome": "100% IaC environments with OIDC-based CI deploys, no long-lived credentials, dev/prod parity, account / subscription topology suitable for audit."
      },
      {
        "slug": "ai-assisted-engineering-workflow",
        "name": "AI-assisted engineering workflow setup",
        "problemsSolved": "Team uses ChatGPT or Copilot ad-hoc but has no agent harness, no codebase-aware tooling, no measurable productivity lift.",
        "situation": "Engineering org curious about the \"3× throughput\" claims, wants concrete practice instead of slideware.",
        "technologies": [
          "Claude Code",
          "MCP",
          "Custom skills and hooks",
          "Multi-agent workflows",
          "Python",
          "TypeScript"
        ],
        "outcome": "Codebase-aware agents that handle boilerplate (handler scaffolding, Spark job templates, DDL, integration tests). Measured throughput uplift. Patterns the team can extend on their own."
      },
      {
        "slug": "recovery-of-stalled-projects",
        "name": "Recovery of stalled data / platform projects",
        "problemsSolved": "Migration or platform build that started ambitious and has been \"almost done\" for months.",
        "situation": "Original architect left, vendor disengaged, or internal team got pulled into other priorities. Stakeholders want a finish line.",
        "technologies": [
          "Whatever the project is on"
        ],
        "outcome": "Honest assessment of what is salvageable. Re-scoped plan with a real finish date. Either I take it across the line or hand back a project the existing team can finish."
      },
      {
        "slug": "fractional-technical-leadership",
        "name": "Fractional technical leadership",
        "problemsSolved": "No senior data or platform voice in the room when key decisions get made; design choices keep being deferred or made by committee.",
        "situation": "Startup or small data org that does not need a full-time staff engineer but needs someone accountable for the technical direction a few days a week.",
        "technologies": [
          "Architecture review",
          "Hands-on prototyping",
          "Hiring support"
        ],
        "outcome": "Decisions get made and documented. Hires get vetted. The team gets a senior pair to push back on bad asks. Bounded engagement, no scope creep."
      }
    ],
    "projectTypes": {
      "accept": [
        "One-time advisor on data or AI projects — short engagements ending in a clear recommendation plus enough work to de-risk it",
        "Delivering a finished, processed dataset — packaged data products the client owns outright",
        "Stalled platforms or hard migrations needing a sole hands-on lead",
        "DApp / smart-contract work with a real product on the other end",
        "Hackathon collaboration — short, high-intensity team work on a concrete deliverable",
        "Fractional technical leadership on data / platform initiatives"
      ],
      "avoid": [
        "Electricity or energy trading work (existing client conflict — firm, not negotiable)",
        "More than two onsite days a week as a regular cadence (kickoff trips and workshops are fine)",
        "Retainers / always-on availability arrangements"
      ]
    }
  }
}