Practice 03 · Data Platforms

    Pipelines that turn raw events into decisions.

    Streaming and batch pipelines, dbt-modeled warehouses and BI dashboards leadership actually opens on Monday.

    Proof
    Millions
    of daily events ingested
    < 15 min
    data freshness on critical KPIs
    100%
    of pipelines under contract tests
    The brief

    Data quality is a contract, not a clean-up job.

    Most data platforms fail at the producer: nobody promised the schema, nobody owns the freshness, and nobody alerts when the numbers drift. We treat data quality as an upstream contract enforced at ingestion, not a downstream rescue mission.

    From real-time event pipelines to dbt-modeled warehouses and executive BI, we build the platform that makes leadership trust the dashboards — and engineering trust the alerts.

    What you walk away with

    Deliverables and the operating posture they buy you.

    Tangible deliverables
    • Ingestion pipelines with schema contracts and freshness SLOs
    • Warehouse layer modeled in dbt with documented lineage
    • BI dashboards (Metabase / Superset / Looker) wired to stakeholders
    • Data quality alerting tied to business owners
    • Lineage graph and access-control documentation
    • Data review ritual onboarded with leadership
    What changes in operations
    Monday 9am

    leadership opens the dashboard without asking analysts

    < 15 min

    p95 freshness on critical KPIs

    Alert

    fires upstream when a producer breaks schema — not after

    Single source

    of truth — one metric definition, one owner

    Capabilities

    What we build into the stack.

    Click any capability to see how we approach it.

    How we approach it

    Real-time pipelines

    Streaming ingestion with schema enforcement, anomaly detection and freshness SLOs.

    • Kafka Connect / Debezium for CDC, Materialize / Flink for derivations
    • Contract tests on every producer with CI gating
    • Freshness and volume anomaly alerts routed to owners
    • Replay tooling for retroactive corrections
    How we approach it

    Data warehousing

    dbt-modeled warehouse with incremental loads, slowly-changing dimensions and partitioning.

    • Layered modelling: staging → intermediate → marts
    • Incremental materializations with late-arriving data handling
    • Documented lineage in dbt docs, published to stakeholders
    • Cost-aware partitioning and clustering
    How we approach it

    Business intelligence

    Executive dashboards with drill-down, scheduled email digests and mobile-first views.

    • One metric, one definition, one owner
    • Drill-downs that match how leadership actually asks questions
    • Scheduled digest emails for non-dashboard users
    • Embedded analytics for product surfaces where useful
    How we approach it

    Data governance

    Lineage tracking, access controls, audit logs and data-quality SLOs with owner alerting.

    • Column-level lineage published to stakeholders
    • Role-based access at the warehouse layer (RLS where supported)
    • Audit logs of who queried what, retained for compliance
    • Data quality SLOs owned by upstream producers
    Workflow

    From source discovery to data-review ritual.

    Five stages — every one ends with someone signing off on what good looks like.

    1. Week 1–2 · Discovery

      Source mapping & question discovery

      We interview the people who ask questions and the people who answer them — then map the data sources required to close the gap.

      DeliverableSource map + KPI question list
    2. Week 2–4 · Contract design

      Schema contracts & freshness SLOs

      For every source we define a schema contract, freshness SLO, owner and alerting policy — enforced in CI at the producer.

      DeliverableContract registry + SLO doc
    3. Week 4–8 · Pipelines

      Streaming + batch build with quality gates

      We build ingestion, validation and transformation pipelines with quality gates that fail loud and route alerts to owners.

      DeliverableProduction pipelines + quality dashboards
    4. Week 6–10 · Warehouse

      dbt modelling with documented lineage

      Layered dbt models, published lineage and documented metric definitions — so the warehouse is browseable, not archaeological.

      Deliverabledbt project + lineage docs
    5. Week 10–12 · BI & ritual

      Dashboards delivered with a data-review ritual

      Executive dashboards delivered with a weekly data review on the calendar — because dashboards no one opens don’t pay rent.

      DeliverableBI dashboards + onboarded review ritual
    Architecture

    How data flows from event to decision.

    Contracts at the producer, lineage through the warehouse, ownership at the dashboard.

    EventsApp DBsSaaS APIsLogsSOURCESCONTRACT GATEIngestionSchema · freshness · ownerCI fails on breachDBT MODELSStagingIntermediateMartsdocumented lineageWarehouseBigQuerySnowflakeDECISIONSBI dashboards · digests

    Sources → ingestion + contracts → staging → dbt marts → warehouse → BI dashboards.

    Technology

    Tools we reach for first.

    Defaults — not dogma. We pick the smallest stack that answers your questions on time.

    Ingestion
    • Kafka Connect
    • Debezium
    • Airbyte
    • Fivetran
    Processing
    • Apache Airflow
    • dbt
    • Spark
    • Flink
    Warehouse
    • BigQuery
    • Snowflake
    • Redshift
    • PostgreSQL
    Streaming
    • Kafka
    • Materialize
    • Redpanda
    BI & viz
    • Metabase
    • Apache Superset
    • Looker
    • Grafana
    How we engage

    Three shapes, one commitment to ownership.

    Pick the engagement that fits your team — every shape ends with your team owning the warehouse.

    Engagement model

    Fixed-scope build · Rescue sprint · Retainer

    A greenfield platform build, a sprint to rescue a stalled pipeline, or a retainer for ongoing data work — delivered as an external team.

    Typical timeline

    8–12 weeks to first cut

    From discovery to live dashboards with a weekly review ritual onboarded.

    Ownership

    100% yours, always

    Warehouse, models, source, intellectual property and runbooks transfer to you. No lock-in, and no bodies to manage.

    FAQ

    Questions we hear before the kickoff.

    If yours isn’t here, ask us directly.

    Start a data build

    Ready for data you can trust?

    Tell us the sources, the questions and the cadence. We’ll come back with a shape and a timeline.