Abay Syzdykov
AVAILABLE FOR NEW ENGAGEMENTS

Transforming Complex Data into Actionable Business Alpha

Hi, I’m Abay Syzdykov — Data Analyst & BI Specialist. I architect automated reporting engines, multi-touch attribution pipelines, and predictive churn models that bridge technical data infrastructure with C-suite commercial decisions.

Download CV (PDF)

Core Tooling & Competencies

SQL (Snowflake / BigQuery / Postgres) Python (Pandas / Scikit / NumPy) Power BI & Advanced DAX Apache ECharts / DataViz ETL & dbt Pipelines Statistical Modeling & A/B Testing
Abay Syzdykov - Data Analyst & BI Specialist

Abay Syzdykov

Data Analyst & BI Specialist

Top Rated
Reporting Automation
100% Zero-Touch
Cost Reduction
+$320K ARR Impact
Interactive Proof of Work

Executive Telemetry & SaaS Unit Economics

Live embedded analytics engine displaying multi-cohort subscriber retention, MRR growth vectors, and unit economic health. Switch metrics and time horizons below.

Monthly Recurring Revenue
$148,500
+18.4% MoM
Net Retention Rate (NRR)
114.2%
+3.1% YoY
Avg Customer LTV
$3,820
+12.6% vs Q1
LTV / CAC Efficiency
4.8x
Top-Decile Benchmark
Telemetry Source: Snowflake / dbt mart
Model: Autoregressive Moving-Average (ARIMA) + Kaplan-Meier Cohort Analysis
Visual Engine: Apache ECharts 5.6
STAR Framework Evidence

Featured Commercial Case Studies

Real business challenges solved through quantitative rigor, production data engineering, and C-suite actionable reporting.

01 FinTech & SaaS Client: B2B SaaS ($12M ARR)
View Interactive Dashboard GitHub Source

Subscriber Cohort Retention & Churn Prediction Engine

Situation & Business Problem (Task)

Subscription churn was rising by 3.2% QoQ without visibility into which user cohorts decayed after month 2. The executive team relied on lagging 30-day billing summaries and lacked proactive leading risk indicators.

Technical Pipeline & Architecture (Action)

Architected automated dbt/Snowflake data models structuring 1.8M subscriber activity logs. Built Python survival analysis (Kaplan-Meier & Cox Proportional Hazards) to isolate core friction milestones. Designed an automated Power BI dashboard with DAX-driven cohort decay tracking and real-time alerts for at-risk accounts.

Snowflake dbt Python (Lifelines/Pandas) Power BI Advanced DAX
Quantified Outcomes (Result)
-18%
Cohort Churn Reduction
Across at-risk mid-tier subscribers
+$320K
Preserved Capital
Annualized Recurring Revenue saved
14h → 0h
Reporting Efficiency
Automated manual weekly spreadsheet tasks
02 E-Commerce & DTC Client: Direct-to-Consumer Brand ($2.4M Ad Spend)
View Interactive Dashboard GitHub Source

Multi-Touch Marketing Attribution & Unit Economics Engine

Situation & Business Problem (Task)

Marketing spend across Google Search, Meta Ads, and TikTok had conflicting channel ROAS due to last-click attribution bias, resulting in over-budgeting low-efficiency campaigns and underfunding discovery channels.

Technical Pipeline & Architecture (Action)

Engineered a first-party Markov Chain multi-touch attribution algorithm in Python. Cleaned and joined 4.2M clickstream events using SQL window functions in BigQuery. Delivered an interactive executive scenario simulator that modeled revenue yield per dollar invested across each channel.

Google BigQuery Python (NetworkX/NumPy) SQL Window Functions Power BI ECharts
Quantified Outcomes (Result)
+23%
Blended ROAS Uplift
Return on ad spend optimized
28%
Budget Reallocated
Shifted to high-converting mid-funnel paths
+$640K
Net Incremental Rev
Directly attributable to optimized mix
03 Supply Chain & Ops Client: Regional Logistics 3PL Network
View Interactive Dashboard GitHub Source

Fulfillment SLA Optimization & Warehouse Intake Forecasting

Situation & Business Problem (Task)

Peak season volume surges caused a 12% SLA fulfillment breach rate, triggering $85K in contractual carrier penalties and overburdening warehouse dispatch teams.

Technical Pipeline & Architecture (Action)

Trained Python ARIMA & Prophet time-series models for parcel intake volume prediction with a 94.2% accuracy rate. Streamlined ingestion pipelines into partitioned Postgres data marts and rolled out live operational Kanban dashboards for warehouse floor leads.

PostgreSQL Python (Prophet/Statsmodels) Automated ETL ECharts Metabase
Quantified Outcomes (Result)
12% → 2.4%
SLA Delivery Breaches
Slashing late dispatches by 80%
+$75,000
Penalty Avoidance
Contractual SLA fees avoided
98.6%
On-Time Dispatch Rate
Sustained during Q4 peak holiday season
Capabilities & Methodology

Engineered for Analytical Rigor

Combining data engineering fundamentals with executive commercial awareness to deliver reports that leaders trust.

SQL & Data Modeling

Complex analytical queries, recursive CTEs, window functions, star schema design, and query optimization for high-cardinality databases.

PostgreSQL Snowflake Google BigQuery dbt Dimensional Modeling

Python Analytics & ML

Exploratory data analysis, statistical tests, cohort decay curves, regression, classification, and time-series demand forecasting.

Pandas NumPy Scikit-Learn Prophet Lifelines

Business Intelligence & DAX

Enterprise Power BI reporting architecture, dynamic DAX measures, calculation groups, row-level security (RLS), and drill-through UX.

Power BI Advanced DAX Tabular Editor Dataflows Executive KPIs

Commercial Decision Support

Translating ambiguous operational problems into structured hypotheses, unit economics tracking (LTV, CAC, Payback), and board presentations.

Unit Economics A/B Testing Churn Mitigation Marketing Attribution Stakeholder Alignment
Delivery Framework

From Raw Ingestion to Executive Action

01

Diagnose & Align

Isolate the commercial bottleneck, interview key stakeholders, and define unambiguous North Star metrics.

02

Model & Transform

Clean raw telemetry, write robust dbt/SQL transforms, and validate data integrity against business edge cases.

03

Predict & Visualize

Build self-serve interactive dashboards and statistical forecasting models with transparent confidence bounds.

04

Automate & Drive ROI

Deploy scheduled alerting, eliminate manual reporting cycles, and present synthesized recommendations.

Direct Engagement

Let’s Build Data Infrastructure That Converts

Have an analytics pipeline bottleneck, need automated reporting, or looking for an experienced Data Analyst? Send an inquiry below.