Reliable pipelines and ingestion
Bring source data into a warehouse, replace fragile manual workflows and make failures easier to find and recover.
APIsPythonAirflowCloud storage
Data engineering · Analytics engineering · BI
Better data.
Better decisions.
We build dependable pipelines, trusted models and reporting that helps growing teams move with confidence. From a single broken workflow to a complete analytics foundation.
From source to decision01 / 03
Sources●
Models●
Insights●
Reliable systems. Clear ownership. Useful outcomes.
The practice
Engineering clarity into complex data.
AyuBee Analytics brings senior, hands-on experience to the work between raw data and a useful decision. The expertise behind the practice spans commerce, marketplaces, enterprise technology and financial products, with experience building and leading analytics systems from ingestion to executive reporting.
We connect architecture to everyday use: reliable ingestion, well-defined metrics, tested models and dashboards teams can actually trust.
7+years in data and analytics engineering
Millionsof daily events handled in prior platform work
End to endfrom source systems to BI delivery
What we do
The right data foundation
for the next decision.
Hands-on delivery shaped around the system you already have, the problem you need solved and the team that will own it afterward.
Models teams can trust
Build tested transformations, dimensional models, semantic layers and shared metric definitions for consistent reporting.
SQLdbtData qualityGovernance
Decision-ready reporting
Turn operational, customer and growth data into useful dashboards, analysis and self-service datasets.
TableauPower BILookerKPIs
Relevant project experience
Built for real operating teams.
Representative delivery experience from prior roles behind AyuBee Analytics. The organizations shown are former employers, not AyuBee clients.
Analytics platform from source to self-service
Platform work across ingestion, dbt transformations, dimensional models and certified analytics marts supported marketing, growth, customer experience and operations with shared KPI definitions and self-service reporting.
S3 · Airflow on Kubernetes · dbt · Snowflake · Redshift · Tableau · Looker
Lineage, quality and ownership at scale
DataHub ownership introduced metadata, lineage, documentation and quality controls across analytics layers, making datasets easier for teams to discover and use.
DataHub · SQL · dbt · CI/CD · Data quality
Models for commercial and risk decisions
Large-scale pipelines and dimensional models supported CRM, telesales, performance analytics, scoring, anomaly detection and fraud-related analysis, with automatically refreshed executive reporting.
Azure Databricks · PySpark · dbt · Redshift · Tableau · Power BI
Quality controls for critical data flows
ETL/ELT workflows, staging layers and fact tables were strengthened with alerts, reconciliation and anomaly checks, alongside tuning and investigation against operational SLAs.
Python · SQL · Talend · MySQL · Tableau · Power BI
Connecting external ad data with warehouse reporting
Python ingestion brought advertising API data through cloud storage into Snowflake. Staging, transformation and aggregate layers connected external network data with internal ad performance reporting and a broad KPI tracker.
Python · APIs · S3 · Snowpipe · Snowflake
Technical portfolio
How we approach the hard parts.
The following are independent interview exercises, presented as demonstrations of method and technical judgment. They were not commercial client projects or production systems for the named companies.
Account lifecycle and active-user models
Designed warehouse models that handle account creation, closure and reopening, then defined daily user activity using account status and a trailing seven-day transaction window.
- Explicit grain, event ordering and irregular lifecycle handling
- Data quality tests and documented metric definitions
- Partitioning and clustering choices for repeatable reporting
Performance, anomaly and experiment analysis
Developed SQL and Python analysis for advertiser IPM, anomaly candidates, daily spend and revenue performance, and an Android creative experiment.
- Deduplication and peer comparison logic
- Late-arriving data, incremental aggregation and legacy attribution SQL
- Statistical comparison with an overall-versus-placement readout
Taking analytical SQL into production
Reviewed a complex data scientist query by first clarifying expected output and source semantics, then mapping failure modes, tests, orchestration and ownership.
- Separate logic defects from reliability concerns
- Plan validation for malformed JSON, duplicates and joins
- Define monitoring, documentation and operational handover
API to warehouse ingestion
A practical pattern for external data: extract through a rate-aware Python client, land raw files in object storage, load into Snowflake and transform into tested reporting tables.
- Idempotent loads and incremental backfills
- Schema drift and data quality controls
- Clear source-to-metric lineage
Technology
Fluent across the modern data stack.
Tools matter when they solve the right problem. We choose for maintainability, performance and the team’s existing architecture.
Data platforms
Snowflake · Amazon Redshift · BigQuery · Azure Databricks · Delta Lake · AWS S3
Engineering
SQL · Python · PySpark · dbt · Airflow · Kubernetes · REST APIs · CI/CD
Governance
DataHub · Data lineage · Data quality · Dimensional modelling · KPI frameworks
Insights
Tableau · Power BI · Looker / LookML · Retool · Funnel and cohort analysis · Experimentation
Start a conversation
Have a data problem worth solving?
Tell us what is unreliable, slow or difficult to maintain. We can start with a focused conversation and define a practical scope.
ayubi939@gmail.com ↗Good starting points
Unreliable pipeline or reporting
Warehouse and dbt modelling
API ingestion and automation
Senior delivery capacity
AyuBee on LinkedIn ↗
Unreliable pipeline or reporting
Warehouse and dbt modelling
API ingestion and automation
Senior delivery capacity
AyuBee on LinkedIn ↗