What do data engineering services actually include?
Four layers, in the order they usually break: ingestion (getting data in from your systems and third-party providers on time), orchestration (scheduling and retrying the jobs that move it, with alerts when they don't), warehousing and modelling (one queryable source of truth instead of reconciled silos), and quality (tests, lineage and audit logs so a number can be trusted and traced). At AgoraData that meant migrating legacy PDI jobs onto Airflow and keeping Snowflake and RDS pipelines reliable under a SOC-2 audit; at Delos it meant ingesting two commercial news providers and the Swiss Parliament's open data into one normalized model.
When should we move from scripts and cron jobs to an orchestrator like Airflow?
When a failed job costs you a morning. Cron is fine while one person can hold every job in their head and re-run it by hand. The moment a pipeline feeds a customer-facing number, an auditor, or a model, you need retries, dependencies, backfills and an alert that fires before your customer notices. That is what an orchestrator buys, and it is usually a two-to-four week migration, not a re-platform.
How do you keep a data pipeline reliable without a full-time data team?
Instrument before you optimize: every job reports freshness, row counts and failures to the same place your engineers already watch. Then remove the hand-held steps, because a pipeline a human has to babysit is not a pipeline. AgoraData's loan-processing flow went from manual handoffs to an automated, instrumented pipeline; Delos runs twenty-plus scheduled tasks on a managed scheduler rather than a box someone has to keep alive. Either we operate it on a retainer or your team does — the runbooks are the same.
How does data engineering connect to our AI roadmap?
AI without a data foundation is a demo. Retrieval needs a clean, indexed corpus; evals need a labelled dataset that stays current; cost control needs per-call telemetry you can query. We build those as data pipelines first, so the agent work that follows has something reliable to stand on — the order AgoraData's AI/ML readiness and Delos' retrieval layer were built in.
Every source, owner, consumer and SLA on one page, plus the failure history. You leave with a written lineage map and the one flow that is costing you the most.
2
Weeks 2–4First pipeline in production
The most fragile flow moves onto an orchestrator with retries, backfills and alerting. Real data, real schedules — no synthetic demo.
3
Weeks 5–8Warehouse & quality
One modelled source of truth, data tests on every load, audit logging where regulators or buyers will ask for it.
4
Week 9+Operate or hand off
Your team runs it with the runbooks we wrote, or we keep operating it on a retainer with monthly freshness and cost reviews. Your call.
After launch
A pipeline is only reliable while someone is watching it.
Cadence
Freshness, row counts and failures reported daily into the channel your team already watches; backfills and schema changes shipped inside the sprint.
Commercial shape
A milestone-priced migration, then a small monthly operations retainer — or your team runs it with the runbooks we wrote. AgoraData's pricing was tied to certification, not headcount.
Reviews
A monthly freshness and cost review; a quarterly data-quality audit with the evidence an auditor or a buyer will ask for.
Hand-off
Orchestrator, warehouse, secrets and audit logs live in your accounts from day one, the way AgoraData kept every credential and ownership record.
~25 paying enterprise customersRead the case study
3 agents restored · platform handed backRead the case study