Data engineering & analytics servicesPipelines and reporting
people stop arguing with.

Ingest, warehouse, models and dashboards that the in-house team can run after we leave. Measured before and after every increment.

Book a consult See our work

Batch → incremental

Nightly runs that finish.

One source of truth

Warehouse and semantic layer.

Dashboards used

By the people who asked.

Engagement

Data & AI · project or retained

Three teams, three revenue numbers. One warehouse and one definition later, the same number everywhere. Example figures.

What you get.

Ingest and warehouse work the in-house team can run after we leave

One source of truth: a warehouse and a semantic layer, documented where your team looks

Dashboards the people who asked for them actually open

A before-and-after measurement on every increment

Engagement: Data & AI · project or retained · Ask about your pipelines →

Good for

— Reporting nobody trusts

— A nightly batch that is now a daily problem

— Numbers that differ by who ran the query

We will say no when

— You want a dashboard before anyone agrees what the numbers mean

— The data does not exist yet and nobody owns collecting it

Stack · only what we have shipped to production

Custom ETLAWS GlueAthenaPostgreSQLTimescaleDBRedshiftPower BIMixpanelAmplitudeFunnel analysis

Where we do this mostFintech & payments →Retail & e-commerce →

From the record.

All case notes →

01 · Life sciences · data

Data & AI

Preclinical research portal for global labs

Problem — Preclinical data was growing exponentially across labs and formats; nobody could compare it, let alone predict from it.

What we did — A compliant cloud architecture with custom ETL to harmonise heterogeneous lab data. Machine-learning models for predicting therapeutic response and model relevance. Dashboards and real-time visualisation for researchers.

Measured — Data analysis time down 60%. 100% of data visible across global teams. Predictive accuracy up.

Custom ETLPythonML modelsCloud architectureDashboards

Client withheld · details on the callRead the case note →

02 · Data · ETL

Data & AI

Marketing data consolidated into ML-ready datasets

Problem — Marketing data scattered across sources, none of it ready for a model.

What we did — An R&D engagement on AWS Glue and Athena consolidating marketing data into ML-ready S3 datasets.

Measured — ML-ready datasets delivered — [ADD SOURCES / VOLUME].

AWS GlueAthenaS3ETL

Client withheld · details on the callAsk about this →

03 · E-commerce · analytics

Data & AI

Funnel analysis and CRO for a large store

Problem — A store with over Rs 95M in sales and thousands of orders did not know where the funnel leaked.

What we did — Deep funnel analysis across 1.15M sessions to find drop-offs before checkout. Conversion-rate work at the points that mattered. A data foundation for budget allocation.

Measured — 2.05% store conversion held at high traffic. Average order value Rs 6,074. Over Rs 95M total sales analysed.

Shopify analyticsFunnel analysisCRO

Client withheld · details on the callAsk about this →

04 · Buildings · IoT

Product

Real-time operational intelligence for 200+ buildings

Problem — Building operators needed live operational data, not last month’s report.

What we did — Product leadership of a telemetry platform delivering real-time operational intelligence across a large building estate.

Measured — Powering 200+ buildings.

IoT telemetryReal-timeOperations

Client withheld · details on the callAsk about this →

How it runs.

The same loop as every engagement

Week 0

Read the pipeline

Sources, schedules, the queries people run by hand, and the two numbers that disagree.

Week 1

The baseline

How long the nightly run takes, how often it fails, and which report nobody trusts. Written down.

Week 3

First increment

The worst pipeline replaced: incremental, idempotent, with a dead-letter queue and a replay.

Week 6

Runs without us

Runbook, alerts and the on-call steps in your wiki; your team runs it for a week while we watch.

Fortnightly

The review

The same numbers as week one, after. Then the next pipeline, or the handover.

Questions we get about this.

The one you have, unless it is the problem. We have shipped on PostgreSQL, Redshift, TimescaleDB and Athena over S3; we will say on the call if yours is the wrong tool.

Yes, and we start from the question the dashboard answers, not the chart. If nobody can say what decision it changes, we build the pipeline first and the dashboard last.

A number from before, the same number from after, and the unit: run time, failure rate, freshness, the gap between two reports. It goes in the review you sign.

The other seven.

All eight end in something running

01Custom software→02Team augmentation→03Data engineering & analyticshere04AI & machine learning→05Cloud & DevOps→06Legacy modernisation→07Mobile→08QA & test automation→

Data engineering & analytics · the call

Bring the report nobody trusts. We will tell you why.

Bring the problem, not a brief. If it is outside what we do well, we will say which of our contacts to call instead.

Book a consult See our work

Or email info@hopsig.com — reply within one working day.

What you get.

Ingest and warehouse work the in-house team can run after we leave

One source of truth: a warehouse and a semantic layer, documented where your team looks

Dashboards the people who asked for them actually open

A before-and-after measurement on every increment

Engagement: Data & AI · project or retained · Ask about your pipelines →

Data engineering & analytics servicesPipelines and reportingpeople stop arguing with.

What you get.

From the record.

Preclinical research portal for global labs

Marketing data consolidated into ML-ready datasets

Funnel analysis and CRO for a large store

Real-time operational intelligence for 200+ buildings

How it runs.

Read the pipeline

The baseline

First increment

Runs without us

The review

Questions we get about this.

Which warehouse?+

Do you do the dashboards too?+

What does measured mean here?+

The other seven.

Bring the report nobody trusts. We will tell you why.

What you get.

From the record.

Preclinical research portal for global labs

Marketing data consolidated into ML-ready datasets

Funnel analysis and CRO for a large store

Real-time operational intelligence for 200+ buildings

How it runs.

Read the pipeline

The baseline

First increment

Runs without us

The review

Questions we get about this.

Which warehouse?+

Do you do the dashboards too?+

What does measured mean here?+

The other seven.

Data engineering & analytics servicesPipelines and reporting
people stop arguing with.