Senior Data Engineer · Build + Consulting

Your business data.
Connected and checked.

We connect your business systems, build data warehouses and automate reporting. Your team gets consistent numbers without collecting spreadsheets and checking them by hand.

StructuredSources brought together
ReportingMetrics your team can use
CheckedErrors and duplicates flagged
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How data reaches your reports

We collect data from your systems, check it and prepare it for reporting. Each step below explains part of that work.

Your data

In business systems and files Where it comes fromPostgreSQL · CRM (Creatio) · ERP (1C / Odoo) · Kafka · REST & OData APIs · BigQuery · CSV.Solves: data trapped in silos.

Collected

Updated on a schedule Automated extractionScheduled incremental syncs, retries and backfills.Reduces the need for manual exports.

Checked

Errors caught before they hit reports Data checksSchema, types, missing values, duplicates and anomaly rules. Records that fail checks are set aside for review.Finds errors before reporting.

Cleaned

Consistent formats across systems TransformationPySpark cleaning · joins across systems · business logic · standardized formats & units.Solves: every team reporting different numbers.

Enriched

Customer groups and business metrics Adds meaning & contextPlain records become useful: customer segments (best / new / at-risk), calculated metrics (conversion, average deal), labels like region or category.Solves: raw data that answers nothing.

Organized

Shared definitions for your metrics ModelingStar schema · fact & dimension tables · one shared business logic · automated tests on every build.Solves: each department counting revenue its own way.

Stored

Fast even on heavy queries Data warehouse and lakePostgreSQL · ClickHouse · BigQuery · MinIO (S3). Partitioning and indexes are chosen for the queries your reports use.Reduces query time and database load.

Dashboards

Sales, customers and team performance Delivered as analyticsPower BI dashboards · custom DAX · automated refresh · executive KPIs.So you get: clear answers you can act on.
bad data filtered out

Still putting reports together by hand?

Tell us which systems you use and what takes the most time.

Discuss your reporting

Before building a dashboard,
check the numbers.

Two reports can show different revenue for the same month. We trace the difference back to the source, agree on calculation rules with your team and add checks to each update. That gives you a way to explain a number and find out when something is wrong.

Agreed calculation rules Checks on each update Alerts when checks fail
See how we work
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Data sources integrated
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Tables modeled in the DWH
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Orchestrated pipelines
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BI reports delivered

Full-stack data engineering

We can build the reporting system or help with a specific part: connecting sources, checking data or fixing a slow report.

Data Integration & Pipelines

We connect databases, business applications and files so your team no longer has to move data between them by hand.

Data Warehouse Design

We bring data from different systems into a warehouse with shared definitions for customers, sales and other business metrics.

Data Quality & Validation

We find duplicates, missing records and conflicting numbers, then set up checks to catch them on future updates.

BI & Analytics

We build reports for the questions your team needs to answer, with a breakdown from overall results to individual records.

AI-assisted analytics

We build tools that let your team ask questions in plain language and check the queries and results behind the answers.

Infrastructure and monitoring

We set up scheduled updates, monitor failures and tune infrastructure for your workload and budget.

How we use AI in engineering

We use AI to draft code, tests and documentation. Engineers review the output, check it against the project requirements and test it before deployment. We agree which tools and data can be used before starting work.

Discuss a project

Less repetitive work

AI helps with boilerplate and first drafts, leaving more time for data logic and testing.

Engineer review

We review generated code and test the results before putting changes into use.

Agreed data access

Tool selection and data access are agreed with you as part of the project setup.

How a project works

We agree on the expected result first, then check the data and reports against it.

01

Discovery

We review your sources, reporting needs and constraints, and agree how to check the result.

02

Architecture

We map how data will move between systems and how the reporting tables will fit together.

03

Build & Test

We build the pipelines and test calculations, data checks and failure handling.

04

Validate

We compare the results with source records and review discrepancies with your team.

05

Deploy & Optimize

We deploy, set up monitoring and adjust performance and running costs.

Projects in practice

See the original problem, what was built and how the results are used.

Funnel PySpark Power BI

Unifying a fragmented sales funnel

CRM and ERP data combined in one sales funnel, with conversion at each stage.

View case study
RFM MinIO Power BI

Customer segmentation for repeat business

Customer groups based on purchase history, refreshed on schedule for marketing reports.

View case study
Anti-fraud SQL Validation

Checking lead registrations

Lead records checked against internal sales and vehicle registrations, with matching cases flagged for review.

View case study
REST API FastAPI Integration

Warehouse data for CRM, ERP and apps

An API gives business applications read access to approved reporting tables.

View case study
DWH Apache Airflow PySpark

Coordinating the nightly warehouse update

Reporting pipelines run in sequence, with a single summary showing success or failure.

View case study
Performance Temperature Density

Tracking lead follow-up

A report shows completed activities, gaps between contacts and lead progress by team and manager.

View case study

The stack we work with every day

Tools we use to collect, store, check and report on data.

Python
SparkApache Spark
AFApache Airflow
S3MinIO
PGPostgreSQL
CHClickHouse
BQBigQuery
BIPower BI
KafkaKafka
APIFastAPI
Docker
Linux
15+ Years Experience
Data quality first

We build and maintain data systems

Our work covers data integration, warehouse design and reporting. That includes a warehouse with 2,000 tables and ten orchestrated pipelines used for daily analytics.

We work through the details that affect your reports: how systems identify the same customer, when data updates and how each metric is calculated. We also set up checks and monitoring so problems can be traced back to their source.

If your reports disagree or take too long to prepare, tell us where the process gets stuck.

Python & PySpark90%
SQL & Data Warehousing88%
Apache Airflow & MinIO / ETL85%
Power BI & DAX80%
Work With Us

Pricing and scope

Choose a starting scope below. We will discuss your sources and reporting needs before work begins.

Monthly Annual Save 20%

Starter

For small projects and individual needs

$800 $640 /month
  • 1 ETL Pipeline
  • Basic Data Warehouse
  • 1 Power BI Dashboard
  • Email Support
  • AI/Agent Integration
  • Priority Support
Discuss a project

Enterprise

For multiple systems and a scope agreed with your team

Custom Custom
  • Unlimited Pipelines
  • Multi-DWH Architecture
  • Full BI Platform
  • Custom AI Agents
  • 24/7 Priority Support
  • Dedicated Slack Channel
Contact us

What would you like to fix in your reporting?

Tell us what you use today and what is not working. We will reply within 24 hours.

Discuss a project →

Tell us about your project

A short description is enough to start: which systems you use and what you need from the data.

Location

Ukraine · Remote-friendly · EU timezone

Response Time

Within 24 hours

We will reply to the email address you provide within 24 hours.