Amsten / Solutions / Data Engineering

Infrastructure — 04

The data foundation
behind every decision.

We build the pipelines, lakehouses, and real-time systems that turn raw data into decision-ready insight — powering analytics, reporting, and machine learning.

Talk to us → See how it works
Data platform orders_daily
Data platform
Pipelines
Lakehouse
Dashboards
Data quality
Pipelines All pipelines healthy
extract_orders → transform → load 4m 12s
sync_customers 1m 48s
events_stream live
284M
Rows / day
4.2m
Freshness p95
0
Failed runs

Why it matters

Decisions are only as good as the data behind them.

01

Data lives in silos

Product events, CRM records, support tickets, payments — scattered across systems that don't talk to each other, with no single source of truth.

02

Reports are always stale

Manual exports and weekly spreadsheet refreshes mean decisions get made on data that's already a week out of date.

03

Nobody trusts the numbers

Without lineage and quality checks, every dashboard becomes a debate about whose numbers are right instead of what to do next.

What we provide

A data foundation every team can trust

Pipelines & orchestration

Reliable pipelines that don't wake you up at 3am

We build ingestion and transformation pipelines with Airflow or Prefect — scheduled, monitored, and self-healing where it matters most.

  • Batch and streaming ingestion from any source

  • DAG orchestration with retries and alerting (Airflow, Prefect)

  • dbt-based transformation and modelling layers

DAG Airflow · orders_daily
extract_orders ✓ 38s
transform · dbt · 14 models ✓ 1m 52s
load → warehouse ✓ 22s
24
DAGs
3
Auto-retries
0
3am pages
Lakehouse SQL
SELECT region, SUM(revenue)
FROM lakehouse.fact_orders
WHERE order_date >= '2026-07-01'
GROUP BY region;
Scanned 84M rows1.2s
84M
Rows scanned
1.2s
Query time
1
Source of truth

Lakehouse architecture

One warehouse, every team querying the same truth

We design lakehouse architecture on Databricks, BigQuery, or Snowflake — structured so analysts, data scientists, and product teams all work from the same governed tables.

  • Lakehouse design on Databricks, BigQuery, or Snowflake

  • Star-schema and dimensional data modelling

  • Access controls and governance across teams

Real-time streaming

See what's happening as it happens

For fraud detection, live dashboards, or instant personalisation, batch isn't fast enough. We build event streaming with Kafka or Kinesis for sub-second visibility.

  • Event streaming with Kafka or Kinesis

  • Real-time aggregation and anomaly detection

  • Live dashboards backed by streaming data

Events per second live
−60snow
12.4k
Events / s
<1s
Latency
2
Anomalies
Data quality 48 tests
fact_orders 100% valid
dim_customers 99.8% valid
events_raw 2 anomalies
48
Tests
99.9%
Valid rows
2
Flagged

Quality & governance

Know exactly where every number comes from

We build automated data quality checks and lineage tracking, so when a number looks wrong, you can trace it to the source in minutes, not days.

  • Automated schema and data quality tests

  • End-to-end lineage tracking (dbt, OpenLineage)

  • BI dashboards on Metabase or Looker

How we build it

From scattered data to a governed single source

1.0 Map

Inventory every data source and owner

2.0 Model

Design schemas and transformation logic

3.0 Pipeline

Build, test, and schedule ingestion

4.0 Monitor

Track quality and alert on drift

Tools we work with

Proven data infrastructure, built around your sources

Airflow & Prefect

Pipeline orchestration

Databricks & Snowflake

Lakehouse & warehousing

Kafka & Kinesis

Real-time streaming

dbt

Transformation & modelling

Metabase & Looker

BI & dashboards

Great Expectations

Data quality testing

Have a project in mind?

Tell us what you're building — we'll scope the right team and timeline.

Talk to us →