Data Management Manager · Legal Reporting · Istanbul, Turkey
Onur
Altıntaşlı
Data Management Manager with 13+ years of experience in Turkey's finance, insurance, and pension sectors. Currently leading legal reporting operations and data governance at Anadolu Hayat Emeklilik — Turkey's largest life insurance and pension company — with end-to-end ownership of statutory compliance pipelines including FATCA, CRS, HAYMER, and Merkez Bankası reporting.
About
Data Engineer &
Governance Leader
Data Management Manager with 13+ years of experience in Turkey's finance, insurance, and pension sectors. Currently leading legal reporting operations and data governance at Anadolu Hayat Emeklilik — Turkey's largest life insurance and pension company — with end-to-end ownership of statutory compliance pipelines including FATCA, CRS, HAYMER, and Merkez Bankası reporting.
Combines deep technical expertise in Oracle PL/SQL, Java, and enterprise integration with hands-on team leadership and strategic data management. MSc in Engineering & Industrial Management from Marmara University..
13+
Years Experience
1M+
App Users
2
Sectors
7+
Certifications
Career
Work
Experience
Assistant Manager, Data Management
Anadolu Hayat Emeklilik · Istanbul, Turkey
- —Own end-to-end legal reporting pipelines for Turkey's largest life insurance and pension company — delivering full regulatory compliance across GEV, HAYMER, Merkez Bankası, FATCA, and CRS obligations with zero tolerance for error or delay.
- —Drive data management strategy and governance across the organization: data quality controls, lifecycle policies, and cross-department data operations.
- —Manage government contribution (devlet katkısı) processes and coordinate internal operational controls to ensure accuracy and auditability of all statutory submissions.
- —Lead and mentor a specialist team, bridging technical execution and stakeholder communication at the management level.
Senior IT Specialist & Software Architect
Anadolu Hayat Emeklilik · Istanbul, Turkey
- —Architected and delivered the GEV legal reporting system — a mission-critical Java application forming the statutory data pipeline between Anadolu Hayat Emeklilik and the Pension Monitoring Center (EGM).
- —Built and maintained enterprise-scale PL/SQL packages and procedures for large-volume financial data processing, ETL pipelines, and BI reporting.
- —Designed and deployed a Microsoft BizTalk Server integration layer to centralize payment procedures across multiple enterprise platforms — eliminating data silos and reducing manual reconciliation.
- —Delivered the FENIKS Oracle migration from AS400: complex ETL development handling mission-critical financial records with zero data loss.
- —Built end-to-end customer churn analytics pipeline and data lake infrastructure using Python and Knime, directly supporting strategic business decisions.
- —Developed Windows Forms, Windows Service, WCF, and C# applications for financial operations, billing, and internal tooling.
System Specialist
Emeklilik Gözetim Merkezi · Istanbul, Turkey
- —Core Oracle DBA and PL/SQL developer for Turkey's national Pension Monitoring Center — maintaining the database infrastructure underpinning pension data for millions of participants.
- —Delivered Oracle Forms applications, data mining solutions, and ERP management tooling for regulatory and operational use.
Solution Developer
Mobinex · Istanbul, Turkey
- —Built iOS and Android applications for Turkey's major financial institutions using the Smartface Financial Services Platform.
- —Developed Akbank Direkt — a full-featured mobile banking app that reached 1M+ active users — delivering the complete suite of banking operations on mobile.
- —Delivered mobile application for Sahibinden.com.
System Developer
Teknosa · Istanbul, Turkey
- —Oracle ERP, CRM, and Siebel system development for one of Turkey's leading electronics retail chains.
- —PL/SQL form design, data mining, and Oracle Discoverer reporting.
Selected Work
Notable
Projects
01
GEV Legal Reporting System
Mission-critical Java application — the statutory pension data pipeline between Anadolu Hayat Emeklilik and Turkey's Pension Monitoring Center (EGM). Ensures full regulatory compliance for millions of pension records with zero tolerance for error.
02
Customer Churn Analysis & Data Lake
End-to-end analytics pipeline for customer churn prediction at Anadolu Hayat Emeklilik. Designed and built the data lake infrastructure from the ground up, enabling data-driven strategic decisions across the organization.
03
FENIKS Data Migration
Enterprise-scale Oracle database migration from AS400. Architected and executed comprehensive ETL scripts handling mission-critical financial records — delivered with zero data loss.
04
BizTalk Payment Centralisation
Microsoft BizTalk Server integration project using Oracle PL/SQL to centralize payment procedures across multiple enterprise platforms — eliminating data silos and unifying financial operations.
05
Akbank Direkt
Full-featured iOS/Android mobile banking app for Akbank, one of Turkey's leading banks. Reached over 1 million active users delivering a complete suite of banking operations.
Expertise
Skills &
Expertise
Data Management & Governance
Database & Backend
Development
Tools & Platforms
Leadership & Process
Credentials
Certifications
Background
Education
MSc Engineering & Industrial Management
Marmara University
2021 — 2023
Graduate-level focus on engineering management, operations, and organizational leadership
BSc Computer Engineering
Eastern Mediterranean University
Honor Certificate · Software Engineering Certification
Writing
Latest
Articles
2026-06-05
EIOPA Convergence in Practice: What Turkish Pension Companies Are Actually Building
EIOPA alignment is framed as a compliance project in Turkish insurance, but the real work is rebuilding data infrastructure that was never designed for the granularity and frequency these frameworks demand. The gap between SEDDK reporting habits and EIOPA-grade pipelines is where the actual engineering happens.
Read →2026-06-04
Payment Centralization Across Enterprise Platforms: The Hidden Complexity of Doing It Right
Centralizing payments looks like a clean consolidation win until you actually own the routing matrix, timing dependencies, and reconciliation surface that come with it. The decentralized mess you replaced starts to look like the easy problem.
Read →2026-06-02
Why Insurance Data Is Structurally Messier Than Banking Data — and How to Handle It
Banking data is transactional and linear; insurance data carries embedded uncertainty, multi-period state changes, and product structures that quietly break standard data quality frameworks. Here is what 13 years of life, pension, and health pipelines taught me about handling it.
Read →2026-05-31
Oracle DBA Skills Every Data Manager Should Have But Rarely Does
A data manager who cannot read an execution plan or interpret wait events is permanently dependent on DBAs they cannot evaluate. That dependency shows up in audit delays, broken pipelines, and architectural decisions made by the wrong people.
Read →2026-05-30
Zero-Error Tolerance in Statutory Submissions: How to Actually Achieve It Operationally
Zero-error tolerance is not a QA checkbox — it is a design constraint that dictates how you architect every pipeline upstream. Here is what actually works after running GEV, HAYMER, FATCA, CRS, and Merkez Bankası submissions in parallel.
Read →2026-05-29
The 9-Year Company Journey: What Staying Teaches You That Job-Hopping Cannot
The tech industry rewards movement, but regulated finance rewards memory. After 13 years at one insurance company, I've learned that depth compounds in ways breadth never will.
Read →2026-05-28
What Changes When You Move from Developer to Data Manager — and What Doesn't
The jump from senior developer to data manager gets sold as a soft skills problem. In regulated finance it's actually a trust architecture problem, and that's where most new managers quietly fail their first year.
Read →2026-05-27
PL/SQL in 2026: Why the Industry Keeps Declaring It Dead and Why It Keeps Being Wrong
Every few years a new generation of engineers declares PL/SQL obsolete, and every few years the statutory reporting pipelines that cannot fail keep running on it. That is not inertia. That is architecture.
Read →2026-05-26
Oracle Performance Tuning for Large-Volume Financial Data: What the Textbooks Skip
Standard Oracle tuning advice was written for OLTP workloads, not for pension valuation runs that scan 200 million policy rows under a six-hour SLA. Here's what actually works when batch windows, bitemporal joins, and regulatory reconciliations meet production.
Read →2026-05-25
What Regulators Actually Look for in Statutory Submissions — From Someone Who Sat on Both Sides
Compliance teams optimize for the wrong things in statutory reporting. After years of reviewing submissions at EGM and a decade preparing them, the patterns that trigger deeper scrutiny are rarely the ones companies prepare for.
Read →2026-05-24
HAYMER: Why Pension Data Pipelines Are Structurally Different from Every Other ETL You've Built
HAYMER submissions don't tolerate the assumptions baked into standard financial ETL. A misaligned timestamp or a missed state transition isn't a data quality ticket — it's someone's retirement balance.
Read →2026-05-21
Government Contribution Reconciliation: The Daily Complexity Nobody Writes About
The Turkish pension system's state contribution looks like a simple 30% match on paper. In production, it's a daily reconciliation problem where every timing mismatch turns into a real money discrepancy with EGM watching.
Read →2026-05-20
FENIKS: What Zero Data Loss Actually Means When You Migrate at Scale
Zero data loss is not a metric you celebrate at cutover. It is a constraint you bake into every schema decision, every reconciliation hook, and every rollback path from day one.
Read →2026-05-18
What BizTalk Taught Me That Modern iPaaS Tools Forgot
BizTalk is mocked as legacy bloat, but it forced architectural habits that today's iPaaS platforms quietly abandoned. The data quality failures we keep firefighting are the direct consequence.
Read →2026-05-17
FATCA/CRS Compliance: What the Data Pipeline Actually Looks Like
Compliance treats FATCA and CRS as a reporting obligation. The data engineers building the pipelines know it is a structural data quality problem dressed in regulatory language.
Read →2026-05-16
What 'Data-Driven' Actually Requires to Be True
Every organization claims to be data-driven. Very few have done the work that 'data-driven' actually requires. Here's the gap between the aspiration and the infrastructure.
Read →2026-05-14
The Governance Conversation You Should Have Before the Audit
Audits don't create data governance problems. They reveal them. The organizations that handle audits well had the governance conversation years before the auditors arrived.
Read →2026-05-12
Kafka in Finance: When Real-Time Actually Matters
Streaming architecture is real and valuable. The question is whether your use case actually requires it — and in regulated financial services, the answer is less often than the tooling conversation suggests.
Read →2026-05-10
Building Data Teams in Regulated Environments
Data team building in financial services requires a different profile than most hiring frameworks account for. The skills that matter most are the hardest to evaluate.
Read →2026-05-08
The Integration Problem Nobody Wants to Solve
Enterprise integration is unglamorous, expensive, and absolutely central to data quality in financial services. It's also the part of the architecture that gets deferred longest.
Read →2026-05-06
Why Data Mesh Doesn't Work the Way You Think
Data mesh solves real problems. But the organizations adopting it are mostly adopting the vocabulary, not the organizational change that makes it work.
Read →2026-05-04
The Real Cost of a Regulatory Submission Error
The penalty is the visible part. The real cost of a regulatory submission error is what it reveals about your data infrastructure — and what it demands to fix it.
Read →2026-05-02
dbt, Spark, or PL/SQL: Why the Tool Choice Is the Wrong Conversation
Every few years the industry produces a new consensus about the right data tooling. Organizations that reorganize around the tool rather than the problem pay for it.
Read →2026-04-30
The Myth of the Single Source of Truth
Every data strategy deck promises a single source of truth. Almost none of them explain what happens when the business has multiple legitimate truths that genuinely conflict.
Read →2026-04-28
What GDPR Actually Changed in Turkish Financial Services
KVKK didn't just add a compliance checkbox. For data teams in Turkish banks and insurance companies, it fundamentally changed what responsible data architecture looks like.
Read →2026-04-26
Why Your ETL Pipeline Is a Liability, Not an Asset
The pipeline that took six months to build and has been running reliably for three years is often the biggest risk on your data team's balance sheet.
Read →2026-04-24
The Data Debt Nobody Talks About
Technical debt gets board-level attention. Data debt accumulates invisibly — until the audit, the regulatory submission, or the executive dashboard shows something nobody can explain.
Read →2026-03-10
AI in Enterprise Data: The Conversation Nobody's Having
Everyone's talking about AI replacing analysts. Nobody's talking about what happens when your data quality is too poor for AI to actually use.
Read →2026-02-18
What I Actually Look for When Hiring Data Talent
After years of hiring in fintech and insurance, I've learned to ignore most CVs and ask a very specific set of questions instead.
Read →2026-01-22
Legacy Systems Aren't the Problem You Think They Are
The industry obsession with 'modernization' is causing organizations to destroy institutional knowledge they don't even know they have.
Read →2025-12-05
Regulatory Compliance Is an Architecture Problem
Most organizations treat compliance as a reporting exercise. The ones that don't get burned spend years rebuilding pipelines from scratch.
Read →2025-10-14
Data Governance: Why Most Companies Are Fooling Themselves
A governance framework that lives in a PowerPoint deck isn't governance. It's theater. Here's how to tell the difference.
Read →Also writing at Teknovole ↗