0:16
“I very much respect the knowledge that you have. You have a lot of things, just in our few communications, that no one else has mentioned. No one else has even made me think about them.”
Data Engineering
Data infrastructure your numbers can trust
Pipelines, warehouses, dbt models, and data quality controls that make analytics and AI features depend on the same trustworthy source.
Why choose us for data engineering?
A dashboard is only as good as the pipeline behind it. We build the contracts, tests and lineage that let every number be traced back to the system it came from.
Batch + ELT pipelines
Ingestion from SaaS APIs, databases, files and event stores is idempotent, with safe backfills and explicit retry semantics, so a failed run can be replayed without duplicating a row or losing one.
Warehouses + lakehouses
Snowflake, BigQuery, Postgres or an open table format is chosen from your volume, concurrency, governance and cost requirements, so you pay for the platform the workload needs rather than the one on the diagram.
dbt transformation layers
Versioned staging, intermediate and mart models ship with tests, lineage, review and generated documentation, so the next analyst can read why a number is what it is.
Streaming + change data capture
Kafka, Kinesis and CDC when second-level freshness is a product need, and reliable hourly batch where it is not, because the system that is cheaper to operate is usually the right one.
Metrics + semantic layers
Governed definitions for revenue, usage, activation and risk mean product, finance and operations use the same number, and the disagreement that used to happen in meetings happens in a pull request instead.
Quality, privacy + lineage
Freshness, volume, schema, access and anomaly controls catch bad data before it reaches a dashboard or a model, which is the difference between trusting a number and checking it.
What we build in data engineering
The platform between your source systems and the people and models that depend on them.
Data pipelines
Ingestion from SaaS APIs, databases, files and event stores into one flow.
- Orchestration with retries and scheduled backfills
- Alerts that say what failed and where
- Incremental loads so reruns do not duplicate rows
Warehouses and lakehouses
Snowflake, BigQuery or well-modelled Postgres, structured before the first table lands.
- Raw, staging and mart layers with clear ownership
- Access boundaries per team and per dataset
- Storage and compute costs visible per workload
ETL and ELT
Raw data absorbed and transformed in the warehouse, or cleaned before it lands.
- Validation, minimisation and de-identification before sensitive data lands
- Transformations versioned, tested and reviewed like code
- Schema changes upstream caught before they break a model
Event streaming
Kafka, Kinesis and change data capture for decisions that lose value within minutes.
- Fraud signals, live product state and time-sensitive alerts
- Exactly-once or idempotent consumers, chosen per stream
- Replay from a checkpoint when a consumer falls behind
Reporting layers
Governed metrics that product, finance and operations read the same way.
- One definition per metric, owned and documented
- Mart models shaped for the questions teams actually ask
- Feeds whichever BI tool you already use
Data quality and lineage
Checks and lineage so a wrong number is caught before it is believed.
- Freshness, volume and schema checks on every load
- Anomaly alerts routed to the owner of the dataset
- Lineage from source to dashboard for every field
Products we shipped in data engineering
Products from our data engineering work. Each result is the one its own case study reports, in its own words.
100+ tournaments run end to end without an administrator touching the standings: 4 roles, 2 bracket formats, one MySQL schema per school.
Esports Tournament Platform
MVP · SaaS
3 data sources normalised; margin drops become tasks priced by what fixing them is worth.
Restaurant Operations Intelligence
SaaS · Data Platforms
65% faster placements, 40% better bed-occupancy accuracy in the NSW pilot; live in ten weeks.
Australia's Homeless Housing Platform
SaaS · Data Platforms
Supplier quoting taken off the procurement team’s desk: the agent reads briefs, images and drawings, then negotiates over WhatsApp and email on its own.
AI Procurement Agent
A.I · Internal Tools
How we build data engineering
Four stages, from an inventory of where data originates to pipelines your team can extend on their own.
Inventory sources + consumers
We map where data originates, who uses it, the decisions it supports, and where definitions currently disagree.
Define contracts + model
Source contracts, ownership, warehouse layers, metric definitions, privacy boundaries, and recovery targets are made explicit.
Build tested pipelines
Ingestion, transformation, orchestration, CI, and backfills ship together: with observable runs from the first production load.
Prove + hand over
We reconcile outputs against source systems, document lineage and runbooks, then train your team to extend the platform safely.
- See our full process
What clients say after the build
Real client reviews, every one checkable at source.
AI Change Management 
0:10“I find him very nice to work with and he actually delivers a very good quality. So I am really happy with him.”
Restaurants Insightful 0:09“The confidence that you're building with me the full product is what I want to go with.”
Self-image 0:07“You guys seem switched on and like you know what you're doing. I've got a good feeling about it.”
GoldenEye 0:05“You guys take a lot of pride and ownership in the work.”
“The team treats our roadmap like their own. Every standup felt like working with our own engineers, not an outside vendor.”
PixelFox “They shipped the production version of what our previous agency had been promising for nine months, without the weekly excuses.”
“They told us what NOT to build. That single conversation re-shaped six months of roadmap and saved us a lot of money.”
NDA - Signed “I had a large project built that needed a very specific understanding and the project was executed perfectly. Great design, great code, no bugs and anything I needed was done incredibly professionally. I would highly recommend and I will definitely be coming back with my…”
$4,000 and above · 11 monthsVerified client review
NDA - Signed “We approached several sellers in our search to get an app developed for our Shopify store. I had a very clear idea in my mind what we wanted and, through [their] expert questioning, [they] absolutely 'got it'. This was further proved through delivery of a…”
$1,500-$2,000 · 4 weeksVerified client review
NDA - Signed “We're working on a Shopify app that retrieves book data from our API and helps online stores and booksellers create book listings faster. This time, we needed to improve the app design and fix functionality issues. In general, we're satisfied with the service and plan…”
$400-$600 · 3 weeksVerified client review
NDA - Signed “This was a special request where we needed to rush this development. [They] complied and finished everything at the agreed time, and even worked until 2AM [their] local time in order to finish this on time. [They] went the extra mile for us with this…”
Verified client review
NDA - Signed “[They were] very patient as we figured out exactly what we needed our app to do. [They were] able to get all the functionality we required, communicated well, and [were] upfront about the cost. We'll definitely be coming back for future projects!”
Verified client review
NDA - Signed “Absolutely fantastic work. This was a tricky app, and [they] did great work. [They] really know [their] way around Shopify and the process for developing a Shopify app, which was a great help to me! [They're] motivated and committed, and delivered a very high-quality app.…”
Verified client review
NDA - Signed “I am very pleased to have worked with [them]! [They were] very detailed and delivered me perfection in an app! I look forward to working with [them] again!”
$3,000-$4,000 · 2 monthsVerified client review
NDA - Signed “Amazing service. Amazing time. Took all input and made the site great even with minor changes along the way. Will certainly try to use again if I'm lucky enough to have [them] work with me again. Thank you for everything.”
Verified client review
NDA - Signed “Very fast and thorough. [They] did exactly what I asked and then went the extra mile to tie up a couple of loose ends. I'm really pleased with the speed and efficiency, and would certainly recommend [them]. Thank you for your help and highly professional…”
Verified client review
NDA - Signed “[They understand] the problem so well and deliver the results super quick. I'll not hesitate to say that I've found a gem. One thousand percent recommended!”
Verified client review
NDA - Signed “Very positive experience. [They are] diligent, hardworking, and [have] good communication skills. Highly recommended.”
Verified client review
NDA - Signed “[They were] very patient, [and] prepared to go the extra mile so I got the result I wanted. Highly recommended.”
Verified client review
NDA - Signed “The project was not easy, literally. [They] did hard work on our project and finished as expected. Would like to work on upcoming new features together. Thank you.”
Verified client review
NDA - Signed “[They are] reasonable, meticulous, [have] a great sense of design, and [are] very responsive. I really enjoyed working with [them] and [they] delivered a beautiful website for me.”
Verified client review
NDA - Signed “Extremely patient and did not stop until what I needed was delivered. Excellent partner to work with!”
Verified client review
NDA - Signed “I didn't expect such great service from someone I found without a referral. I got lucky and I hope to work with [them] again in the future.”
10 daysVerified client review
NDA - Signed “Bought this to create a website for my startup company. Results were above and beyond what I was expecting! Quick to answer, and always gave great input and ideas!”
Verified client review
NDA - Signed “We are very impressed with the service and the delivery and will use them again!”
Verified client review
NDA - Signed “Great work and completed exactly as requested!”
Verified client review
NDA - Signed “100% satisfied. Got more than expected.”
Verified client review
NDA - Signed “Great communication, great result.”
Verified client review
NDA - Signed “Thank you so much for such outstanding work.”
Verified client review
Data engineering questions
We favour the simplest data architecture that can meet your reliability, governance, and freshness requirements.
A well-modelled Postgres is enough for many teams. We recommend Snowflake or BigQuery when scan volume, concurrency, workload isolation, governance, or elastic compute justifies the extra platform. We do not introduce a warehouse to decorate the architecture diagram.