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Data Engineering & Analytics

Reliable pipelines, a warehouse you can trust and dashboards people use: the data foundation every AI feature needs.

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An outlined brain built from thousands of small violet, amber and white tetrahedra on black

The problem

Reports rebuilt by hand every month. Numbers that disagree between tools. AI projects that stall because the data is not clean, joined or fresh. Fintech teams reconciling transactions in spreadsheets. The fix is not another dashboard; it is a pipeline with tests, a modeled warehouse and metrics everyone defines the same way.

What we build

  • Ingestion from apps, APIs, databases and files into pipelines with tests and alerts
  • A modeled warehouse (star schemas, dbt, documented) with self-serve metrics and dashboards
  • AI-ready feature and knowledge layers, plus governance: access, lineage and PII handling

ETL/ELT pipelines (batch and streaming), Data modeling, Warehouse and lakehouse design, BigQuery, Snowflake, ClickHouse, Postgres, dbt, Orchestration (Airflow, Dagster), Event streaming (Kafka, webhooks), Data quality and observability, BI (Metabase, Looker, Power BI), Reverse ETL, Fintech data: reconciliation and reporting

How it works

  1. 01

    Data audit and metric definitions

    Every source, every metric, one agreed definition each. The disagreements surface here, not in the board meeting.

  2. 02

    Pipeline and model design

    Batch or streaming per source, warehouse layout, naming, tests and ownership.

  3. 03

    Build with tests and CI

    Pipelines, dbt models and data-quality checks ship through CI with monitoring from the first run.

  4. 04

    Dashboards and training

    Dashboards on the modeled layer, and the training so your team answers its own questions.

  5. 05

    Operate

    SLAs, cost control and evolution as sources and questions change.

Work that proves it

Adjacent proof: these products show the underlying skills (document extraction at scale, durable event delivery, metrics pipelines behind monitoring, CRM data integration), not data platforms we built end to end.

  • PDFData home page: an extracted invoice card beside a review queue of ready and flagged documents

    PDFData

    AI invoice and receipt processing to Excel, CSV and JSON

    Designed and developed for the client: extraction pipeline, bulk processing and the export and API surface.

  • Webhook Relay home page: forwarding webhooks anywhere, beside a wireframe globe of connections

    Webhook Relay

    Webhook and API infrastructure with SOC 2 Type II, SSO and audit logs

    Designed and developed for the client: webhook forwarding and replay, tunnels, and the audit and access-control surface.

  • Phare home page: uptime monitoring, alerts, analytics and status pages on one platform

    Phare

    Monitoring, incident management and status pages

    Designed and developed for the client: monitoring checks, incident flow and public status pages.

What you get

  • Source inventory and data contracts

  • Pipeline code and orchestration

  • Warehouse models and documentation

  • Data-quality tests and alerts

  • Dashboards

  • Runbook

  • Cost report

Questions

Which warehouse is right for us?
Postgres when the data fits and the team already runs it; BigQuery or Snowflake when volume, concurrency or separation of storage and compute matter; ClickHouse for event-heavy analytics. We recommend the cheapest option that meets the next two years of questions, and we design the models so a move later is a migration, not a rewrite.
Batch or streaming?
Batch by default: it is cheaper, simpler to test and enough for most reporting. Streaming earns its place when a decision depends on data that is minutes old, such as fraud checks or live operations. Most clients run both, with streaming limited to the few sources that need it.
How do you keep data quality high?
Tests on every model (uniqueness, nulls, referential checks, accepted ranges), freshness monitors on every source, and alerts that reach the owner named for that dataset. Failures block downstream models instead of silently corrupting dashboards. Every incident gets a short write-up and, usually, a new test.
How does this connect to AI?
Clean, joined, fresh data is what an AI feature reads. The warehouse feeds retrieval indexes, feature tables and evaluation sets, so agents answer from the same numbers the dashboards show. Our Data & AI Infrastructure service picks up where the warehouse ends.
What does it cost on cloud free tiers versus at scale?
Small teams often run pipelines, a Postgres warehouse and Metabase inside free or near-free tiers. Costs grow with data volume, query concurrency and streaming. We report cost per source and per dashboard monthly, so growth is a decision rather than a surprise.
Who owns the pipelines after handover?
You do. Code lives in your repository, infrastructure in your cloud accounts, and the documentation and runbook are written for your team. We can operate it under a maintenance agreement, or hand it over completely with training.

Not sure where to start?

Describe the problem in a few lines. You get a straight answer on what we would build and how long it takes.

Book a 5-minute growth call
Book a 5-minute growth call