Engineering service
Data ingestion & API integrations
Schedulers, retries, and normalised pipelines from ads APIs, social platforms, and internal services — built for scale and observability.
- Orchestration
- Temporal · queues · DLQ
- APIs
- Meta · TikTok · social surfaces
- Typical slice
- 3–8 weeks per platform bundle
In depth
Ingestion is product infrastructure: if pipelines are flaky, your AI features are flaky. I build schedulers and workers that respect rate limits, rotate tokens safely, and dead-letter the messy edge cases instead of silently dropping data.
Parker AI is the reference build here: Temporal workflows for multi-source marketing and social signal, Meta Marketing API clients with permission-aware fetchers, and normalised schemas that feed both analytics and hybrid retrieval.
What you get
Workflow design: schedulers, fan-out, retries, and idempotency keys
API clients with pagination, backoff, and rate-limit discipline
Canonical schemas and migrations for downstream features
Metrics and alerts: freshness, error budgets, and ingestion lag
Operational runbooks for token rotation and incident response
How we work
Inventory sources
Auth modes, quotas, and what “fresh enough” means per feed.
Define the contract
Stable IDs, versioning, and how failures surface to humans.
Implement workers
Temporal or equivalent with replay-safe logic.
Prove reliability
Soak tests, backoff behaviour, and DLQ triage workflows.
Examples & past work
Outcomes you can expect
- Run multi-source ingestion with backoff, dead-letter queues, and clear ownership of failures.
- Integrate Meta Marketing API and social surfaces with rate-limit-aware, token-safe clients.
- Produce stable schemas that feed analytics, retrieval, and downstream AI features.
Questions, answered
Can you ingest from our private warehouse instead of public APIs?
Yes — the same patterns apply: idempotent workers, clear ownership of schema drift, and observability on freshness.
How do you handle Meta permission changes or token expiry?
Explicit token lifecycle, rotation paths, and fetchers that degrade safely with alerts — not silent partial data.
What is the handoff to the ML or retrieval team?
Documented schemas, sample payloads, and versioned contracts so embeddings and agents consume stable objects.
Book a free 30-minute discovery call to investigate your work and needs
I will map constraints, risks, and a practical first milestone — whether that is agents, retrieval, ingestion, extensions, or full-stack SaaS delivery.
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