Can Goktug Ozdem

Achieved Top Rated status on Upwork with 100% client satisfaction, delivering
40+ successful projects and generating $500K+ in total revenue.
Built and delivered end-to-end data solutions for clients in e-commerce, SaaS,
logistics, and other industries.
Built data warehousing (Snowflake, BigQuery), ETL pipelines (Rivery, Python),
and dashboard development (Power BI, Google Data Studio, Metabase).

As a Data Architect at Beer Hawk, I built data warehousing on Snowflake, ETL
pipelines (Snowpipe, Rivery, Knime), and dashboard development with Power BI.
Orchestrated end-to-end ETL workflows using Apache Airflow, designing and
managing 50+ DAGs to automate data ingestion, transformation, and loading
into Snowflake, ensuring daily refreshes completed within SLAs
Optimized query performance on multi-terabyte fact tables in Snowflake by
implementing micro-partition pruning and clustering strategies, reducing
average query execution time by 68% and scan volume by 85%.
Designed and enforced clustering keys on high-cardinality columns
(order_date, customer_id, event_type) across 15+ analytical tables, enabling
efficient partition elimination and minimizing data scanned during frequent BI
and ad-hoc reporting workloads.
Reduced compute credit consumption by 40% through proactive pruning
aware query design and elimination of full table scans in ETL and dashboard
pipelines.
Built internal monitoring dashboards tracking partitions scanned vs. total,
pruning efficiency, and clustering health — adopted as standard by the data
platform team.
Designed and implemented KPI frameworks (CLTV, Churn Rate, CAC, AOV),
increasing reporting clarity and decision speed for client stakeholders by 30-
50%.
Created over 20 operational reports (daily, weekly, monthly), providing visibility
into business performance and supporting data-driven decision-making.
Performed in-depth data analysis using Python (NumPy, Pandas) improving
client outcomes and campaign efficiency.
Built an automated pipeline that fetched logistics data via hourly API calls,
detecting late deliveries and triggering Slack notifications.
Helped clients reduce delivery-related complaints by 25% and increased repeat
purchase rates through personalized promotions.

seconds to 5 seconds per query).
Ensured 99.9% data uptime and integrity across systems, supporting 100, 000+
monthly transactions without data loss.
Reduced data processing costs by 15% ($10, 000 annually) by optimizing ETL
processes and consolidating redundant data sources.
Enabled seamless integration with third-party tools (e.g., Shopify, HubSpot),
supporting a 25% increase in operational efficiency for data-dependent teams,
as measured by task completion times.

KPI Calculation
Calculated and monitored critical e-commerce KPIs to assess performance and
guide strategy:
Customer Lifetime Value (CLTV): Increased CLTV by 12% over six months by
identifying high-value customer segments and optimizing retention strategies,
contributing an estimated $250, 000 in additional revenue.
Churn Rate: Reduced churn by 15% (from 20% to 17%) by analyzing
cancellation patterns and implementing targeted onboarding campaigns,
retaining ~1, 200 customers annually.
Average Order Value (AOV): Boosted AOV by 8% ($75 to $81) through upselling
recommendations informed by purchase data analysis, adding $180, 000 in
yearly revenue.
Customer Acquisition Cost (CAC): Lowered CAC by 10% ($50 to $45) by refining
marketing channel attribution, saving $75, 000 annually on acquisition spend.

The Credit Pros
Designed and implemented a scalable data architecture using AWS technologies
to support The Credit Pros business objectives:
AWS Redshift: Built a centralized data warehouse on Amazon Redshift to handle
high-volume transactional and analytical workloads, supporting 500, 000+
monthly records. Optimized table designs with sort and distribution keys,
improving query performance by 60% (average query time reduced from 8
seconds to 3.2 seconds).
AWS S3: Utilized Amazon S3 as the primary data lake for storing raw and
processed data, managing 10TB of structured and unstructured data with
lifecycle policies to reduce storage costs by 20% ($15, 000 annually).
AWS RDS: Deployed Amazon RDS (PostgreSQL) for operational databases,
ensuring 99.99% uptime for real-time transactional data and enabling seamless
integration with our credit repair platform, supporting 1, 000+ daily user
interactions.
AWS Glue: Developed 15+ serverless ETL workflows using AWS Glue to integrate
data from CRM systems, financial APIs, and third-party credit bureau feeds,
reducing data processing time by 40% (from 2 hours to 72 minutes per batch).

Developed and maintained 15+ interactive dashboards and financial reports
using Oracle Business Intelligence (OBIEE) for senior management and
department heads.
Built and managed the Physical, Business Model, and Presentation layers in
OBIEE, ensuring a robust, scalable, and high-performing reporting environment.
Connected to multiple data sources and implemented ETL pipelines to populate
the data warehouse with accurate and timely data for daily operations.
Optimized 30+ complex SQL queries, reducing dashboard load times by up to
40% and improving overall user experience.
Managed user roles and row-level security, enabling secure and personalized
access to data for over 100 users.
In addition to major BI projects, regularly completed 3-5 Jira tasks per week,
ensuring continuous delivery of enhancements, bug fixes, and stakeholder
requests.

Developed over 20 dashboards and reports for key insurance departments
including Sales, Marketing, Agencies, Reassurance, and Claims using Oracle
Business Intelligence.
Wrote and maintained 50+ PL/SQL procedures to create tables, views, and
materialized views, processing millions of records to prepare clean, analysis
ready datasets.
Prepared and transformed data by writing PL/SQL procedures to create tables,
views, and materialized views to support reporting needs.
Helped the company build its first CRM system by aggregating and organizing
historical customer data, enabling a single source of truth and creating a
company-wide customer memory.