In the tech world, information is power. For developers in South Korea, knowing your worth is the first step toward effective self-advocacy. While Silicon Valley salaries dominate the headlines, the local reality is different.

Below, we translate salary data into practical benchmarks and a simple way to vet employers using three sources from the Korean HR ecosystem: JobPlanet, Programmers, and Wanted.

The Benchmark: Understanding the Median​

When we look at a developer with 5 years of experience (YOE), the reported range varies by platform.

• JobPlanet (aggregate data): With over 2 million registered salaries, the median is 39M KRW for 5 YOE developers. The lower quartile is 32M KRW, and the upper quartile is 48M KRW.
• Programmers (2023 survey): With a dataset of 4,000+ developers, the median is 55M KRW for the same experience level.

Why the gap? JobPlanet includes a broader mix of companies (including traditional SMEs). The Programmers survey tends to reflect developers from more tech-centric startups and “platform” companies (e.g., Naver, Kakao), where compensation is more aggressive.

The Specialized Premium: Machine Learning​

If you’re moving into specialized fields like Machine Learning (ML), both the floor and ceiling rise.

At 5 YOE, an ML Engineer’s salary shifts upward:

• Lower quartile: 38M KRW
• Median: 48M KRW
• Upper quartile: 59M KRW

This doesn’t mean Korean salaries match US counterparts—but it does show that specializing in high-demand fields is one of the most reliable ways to increase your market value locally.

How to Vet a Potential Employer (The “Wanted” Strategy)​

Benchmarks are useful, but they only matter if you apply them to a specific job opening. If you’re reviewing a company on Wanted, use the Wanted OneID dashboard to extract four signals before you apply:

1. Expected salary: Does the company’s average pay align with the medians above?
2. Revenue: Is the company growing? Stagnant revenue usually means limited room for raises next year.
3. Headcount: Is the team expanding or shrinking?
4. Turnover rate: A quick culture/burnout “vibe check.” High turnover at a high-paying company can be a red flag.

Summary: Advocate for Yourself​

The US-style “100k+ USD starting salary” is rare in the Korean market. But you can avoid lowball offers by using these tools to target employers that are financially healthy—and able to pay at the level you can reasonably justify.

Your toolkit​

• JobPlanet: Market “floor” and overall averages.
• Programmers survey: Compensation visibility for top-tier tech talent.
• Wanted (OneID): Company-level due diligence (growth + retention).

Realistic expectations don’t mean lower goals, they mean better plans. Use this data to advocate for yourself and navigate your career with your eyes open.

AI Engineer leading the full cycle — from LLM agents to data quality.

Skills​

Python (Django/FastAPI) | LLM/LangChain | SQL (MySQL/Snowflake) | GitHub/Jira | Claude Code | AI Agents

Experience: 3 Years​

ML/AI Engineer​

eMoldino, Gangnam — April 2025 - Present

• Designed and deployed end-to-end machine learning pipelines for predictive modeling in the manufacturing domain. [Python, AWS]
• Translated business requirements from cross-functional stakeholders — product, sales, and hardware — into technical specifications for AI-driven solutions.
• Optimized data lifecycle management processes including large-scale data processing, cleansing, and integrity validation to secure high-quality data sources for AI/ML model performance. [Snowflake, SQL]
• Designed an LLM-based automated code health and vulnerability detection solution, proactively mitigating security risks and technical debt through autonomous architectural conformance reviews. [Amazon Bedrock, Claude, LangChain]
• Analyzed existing data to derive and visualize key metrics and trends, then facilitated alignment between decision-makers in both Korean and English to drive tangible project progress.

Data Scientist​

Citek Systems , Changwon — March 2023 - November 2023

• Developed an AI model to predict CNC manufacturing anomalies based on vibration data. [Random Forest, CNN]
• Built ETL pipelines to process and load sensor data generated by smart factory equipment. [Pandas, SQL]
• Deployed real-time machine learning models as RESTful APIs to support inference in production environments. [Flask, Scikit-learn, Postman]
• Built supervised learning and deep learning ensemble models for manufacturing defect prediction. [XGBoost, LSTM]
• Detected chatter (faulty machining) in machine tools using current data and CNN-based anomaly detection. [TensorFlow]
• Applied techniques from research papers to improve and innovate existing in-house products.
• Mentored junior web developers on version control, testing, and debugging workflows. [Git, Figma, VSCode]

Full-Stack Developer​

Ezgeo, Changwon — July 2022 - November 2022

• Developed a full-stack application for a government-funded smart factory machine vision project. [Django, JavaScript, SQL, Linux]
• Implemented a compact MES (Manufacturing Execution System) with container-based deployment and a web management interface. [Docker, Portainer]
• Integrated image analysis and video processing capabilities into an in-house web application. [OpenCV]
• Collaborated with an ML engineer to integrate deep learning inference into enterprise application software. [TensorFlow]
• Planned development sprints based on project requirement documents and research materials.

Full-Stack Developer (Senior Researcher)​

GSF Systems , Changwon — March 2022 - July 2022

• Built a full-stack dashboard for displaying smart farm cultivation data to support vertical farm operations. [SQL, Python, Django]
• Conducted research on vertical farming, hydroponics, and indoor agriculture to support overseas business development.
• Authored investor pitch decks and consumer product presentations at CES 2022, contributing to a CES Innovation Award win in 2023.
• Translated and researched export materials and basic IR/accounting documents related to the controlled environment agriculture industry.

Full-Stack Developer​

Haemilsoft , Ulsan — March 2021 - September 2021

• Developed a full-stack smart farm monitoring platform using enterprise-grade tools. [SQL Server, Java Spring, Azure TFS]
• Introduced and led adoption of a collaborative UI design tool within the development team. [Figma]
• Interpreted English documentation, tutorials, and error codes for libraries and frameworks used by the team. [SSMS, Eclipse, Gradle]
• Monitored an in-house EMS (Energy Management System) for tracking energy production and incidents at solar power plants.

Education​

Associate of Science — Biology​

Douglas College, Canada — September 2014 - November 2016

Master of Science — Artificial Intelligence​

University of Colorado Boulder, USA — June 2025 - June 2027 (Expected)

LLM 에이전트부터 데이터 품질까지, 전 주기를 리딩하는 AI 엔지니어

기술​

파이썬 (Django/FastAPI) | LLM/LangChain | SQL (MySQL/Snowflake) | GitHub/Jira | Claude Code | AI Agents

경력: 3년​

ML/AI 엔지니어​

이몰디노, 강남 — 2025년 4월 - 재직중

• 제조 도메인의 예측 모델링을 위한 엔드투엔드 머신러닝 파이프라인을 설계 및 배포했습니다. [Python, AWS]
• 제품, 영업, 하드웨어 등 유관 부서의 비즈니스 요구사항을 AI 기반 솔루션의 기술 사양으로 전환했습니다.
• 대규모 데이터 처리, 정제, 무결성 검증을 포함한 데이터 라이프사이클 관리 프로세스를 최적화하여 AI/ML 모델 성능을 위한 고품질 데이터 소스를 확보했습니다. [Snowflake, SQL]
• LLM 기반 자동화 코드 헬스 및 취약점 탐지 솔루션을 설계하여, 자율적 아키텍처 정합성 검토를 통해 보안 리스크와 기술 부채를 사전에 완화했습니다. [Amazon Bedrock, Claude, Langchain]
• 기존 데이터를 분석하여 핵심 지표 및 트렌드를 도출하고 시각화한 후, 한영 이중 언어로 의사결정권자 간의 이견을 해소하고 프로젝트의 실질적 진행을 이끌어냈습니다.

데이터 사이언티스트​

씨테크시스템(주), 창원 — 2023년 3월 - 2023년 11월

• 진동 데이터를 기반으로 CNC 제조 이상을 예측하는 AI 모델을 개발했습니다. [Random Forest, CNN]
• 스마트 공장 장비에서 생성된 센서 데이터를 처리 및 적재하는 ETL 파이프라인을 구축했습니다. [Pandas, SQL]
• 실시간 머신러닝 모델을 RESTful API 형태로 배포하여 운영 환경에서의 추론을 지원했습니다. [Flask, Scikit-learn, Postman]
• 제조 결함 예측을 위한 지도학습 및 딥러닝 앙상블 모델을 구축했습니다. [XGBoost, LSTM]
• 전류 데이터와 CNN 기반 이상감지를 활용하여 공작 기계의 불량 가공(채터링)을 감지했습니다. [TensorFlow]
• 연구 논문에 서술된 기술을 적용하여 기존 자사 제품을 개선 및 혁신했습니다.
• 후배 웹 개발자를 대상으로 버전 관리, 테스트 및 디버깅 워크플로우를 멘토링했습니다. [Git, Figma, VScode]

풀스택 개발자​

이지지오(주), 창원 — 2022년 7월 - 2022년 11월

• 정부 지원 스마트 공장 머신 비전 프로젝트를 위한 풀스택 애플리케이션을 개발했습니다. [Django, JavaScript, SQL, Linux]
• 컨테이너 기반 배포와 웹 관리 환경을 갖춘 소형 MES(제조실행시스템)를 구현했습니다. [Docker, Portainer]
• 이미지 분석 및 영상 처리 기능을 자사 웹 애플리케이션에 통합했습니다. [OpenCV]
• 딥러닝 추론 기능을 엔터프라이즈 애플리케이션 소프트웨어에 통합하기 위해 ML 엔지니어와 협업했습니다. [TensorFlow]
• 프로젝트 요구사항 문서 및 연구 자료를 기반으로 개발 스프린트를 계획했습니다.

풀스택 개발자 (주임 연구원)​

지에스에프시스템(주), 창원 — 2022년 3월 - 2022년 7월

• 수직 농장 운영을 위한 스마트팜 재배 데이터 표시용 풀스택 대시보드를 구축했습니다. [SQL, Python, Django]
• 수직 농장, 수경 재배 및 실내 농업에 대한 연구를 수행하여 해외 사업 개발을 지원했습니다.
• CES 2022에서 투자자 대상 피치 덱 및 소비자 제품 프레젠테이션을 작성하여, 2023년 CES 혁신상 수상에 기여했습니다.
• 제어 환경 농업 산업 관련 수출 자료 및 기본 IR/회계 문서를 번역 및 조사했습니다.

풀스택 개발자​

해밀소프트(주), 울산 — 2021년 3월 - 2021년 9월

• 엔터프라이즈급 도구를 활용하여 풀스택 스마트팜 모니터링 플랫폼을 개발했습니다. [SQL Server, Java Spring, Azure TFS]
• 개발팀 내 협업 UI 디자인 도구를 도입하고 활용을 주도했습니다. [Figma]
• 팀에서 사용하는 라이브러리 및 프레임워크의 영문 문서, 튜토리얼, 오류 코드를 해석했습니다. [SSMS, Eclipse, Gradle]
• 태양광 발전소의 에너지 생산 및 사고 추적을 위한 자사 EMS(에너지 관리 시스템)를 모니터링했습니다.

교육​

준학사 - 생물학과​

Douglas College, 캐나다 — 2014년 9월 - 2016년 11월

석사 - Artificial Intelligence​

University of Colorado Boulder, 미국 — 2025년 6월 - 2027년 6월 (졸업 예정)

1. Beginner​

Skills:​

• Basic understanding of server-side programming languages (e.g., Python, Node.js, Java, Ruby).
• Familiarity with HTTP protocols, request/response cycles, and basic client-server architecture.
• Ability to set up a simple server using frameworks like Flask, Express, or Django.
• Basic understanding of databases (SQL or NoSQL) and how to perform CRUD operations (Create, Read, Update, Delete).
• Awareness of RESTful API concepts and how to create simple endpoints.

Example Tasks:​

• Setting up a simple web server that responds to HTTP requests.
• Writing API endpoints that interact with a database.
• Implementing basic user authentication and handling form data.

2. Intermediate​

Skills:​

• Proficient in designing and implementing RESTful APIs with CRUD functionality.
• Understanding of relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Redis), including schema design, relationships, and indexing.
• Familiar with middleware, routing, and handling file uploads.
• Knowledge of authentication methods like OAuth, JWT, and sessions.
• Experience with version control systems (e.g., Git) and basic knowledge of continuous integration and deployment (CI/CD).

Example Tasks:​

• Developing an API for user management (e.g., authentication, authorization).
• Designing a relational database schema and optimizing queries.
• Setting up middleware for logging, error handling, and security in a web application.
• Connecting your backend with external services via APIs (e.g., payment gateways, third-party APIs).

3. Advanced​

Skills:​

• Expertise in building scalable and secure APIs, including complex query optimization, advanced authentication/authorization (e.g., SSO, RBAC).
• Knowledge of microservices architecture and ability to design and develop microservices-based applications.
• Proficient in using messaging queues (e.g., RabbitMQ, Kafka) for asynchronous processing and communication.
• Experience with cloud infrastructure (e.g., AWS, Google Cloud, Azure), containerization (Docker), and orchestration tools (Kubernetes).
• Understanding of caching strategies, load balancing, and scaling backend systems to handle high traffic.

Example Tasks:​

• Designing and deploying a microservices-based architecture with services that communicate asynchronously.
• Setting up continuous integration/continuous deployment (CI/CD) pipelines for automated testing and deployment.
• Implementing caching strategies (e.g., Redis, Memcached) to optimize API performance.
• Designing highly available and scalable systems using cloud services and containerization.

4. Expert​

Skills:​

• Mastery of distributed systems, including managing data consistency, eventual consistency, and CAP theorem implications.
• Expertise in backend architecture patterns (e.g., event-driven architecture, CQRS, serverless) for complex and high-traffic systems.
• Deep knowledge of security best practices, including encryption, secure communication, and data protection in large-scale applications.
• Extensive experience with database replication, sharding, and high availability setups.
• Ability to lead backend development teams, perform code reviews, and ensure code quality standards.
• Familiarity with DevOps tools and practices, including Infrastructure as Code (IaC) and full automation of deployment pipelines.

Example Tasks:​

• Architecting large-scale distributed systems with fault-tolerant and highly available components.
• Implementing advanced security mechanisms like end-to-end encryption and secure API gateways.
• Leading a backend development team, defining project architecture, and overseeing codebase and deployment strategies.
• Optimizing backend services to handle millions of users or high-volume real-time data streams.

1. Beginner​

Skills:​

• Basic understanding of statistics and data analysis.
• Familiarity with spreadsheets or basic data manipulation tools (e.g., Excel).
• Ability to use simple data visualization tools (e.g., Excel, Google Sheets, or Python’s matplotlib and seaborn).
• Introductory knowledge of programming (Python or R) and basic libraries (e.g., Pandas, NumPy).
• Basic knowledge of data types (structured, semi-structured, and unstructured data).

Example Tasks:​

• Plotting simple graphs (bar charts, line graphs) to visualize data.
• Calculating mean, median, mode, variance, and other basic statistical metrics.
• Loading and cleaning small datasets.

2. Intermediate​

Skills:​

• Proficient in data wrangling: loading, cleaning, and transforming data using libraries like Pandas, NumPy, or R’s dplyr.
• Good understanding of probability, statistical testing (e.g., hypothesis testing, confidence intervals), and distributions.
• Basic knowledge of machine learning algorithms (e.g., linear regression, decision trees) and their applications.
• Experience with data visualization libraries (e.g., matplotlib, seaborn, or ggplot2).
• Ability to perform exploratory data analysis (EDA) and extract insights from datasets.
• Familiarity with supervised and unsupervised learning concepts.

Example Tasks:​

• Cleaning and transforming large datasets using Pandas or NumPy.
• Creating visualizations for data distributions and relationships (scatter plots, histograms).
• Implementing and evaluating simple machine learning models like linear regression or K-means clustering.
• Performing A/B testing or statistical analysis on datasets.

3. Advanced​

Skills:​

• Proficient in implementing complex machine learning algorithms (e.g., random forests, gradient boosting, neural networks) using libraries like scikit-learn, TensorFlow, or PyTorch.
• Strong understanding of feature engineering, hyperparameter tuning, model evaluation, and optimization techniques.
• Experience working with large-scale datasets and using cloud platforms for data storage and computation (e.g., AWS, GCP, Azure).
• Familiarity with big data tools and frameworks (e.g., Hadoop, Spark).
• Ability to work with databases (SQL) and unstructured data (e.g., text data with NLP).
• Knowledge of deep learning and more advanced topics like natural language processing (NLP), reinforcement learning, or computer vision.

Example Tasks:​

• Building and fine-tuning machine learning models for production.
• Performing sentiment analysis on text data using NLP techniques.
• Creating predictive models using time series analysis or deep learning methods.
• Implementing machine learning pipelines for automated model training and deployment.

4. Expert​

Skills:​

• Mastery of complex algorithms and advanced techniques, such as deep learning architectures (e.g., CNNs, RNNs, Transformers) or reinforcement learning.
• Deep understanding of data science workflows, MLOps (machine learning operations), and the deployment of machine learning models in production environments.
• Expertise in using cloud platforms, distributed computing, and handling real-time data streams.
• Strong ability to create custom machine learning models, handle imbalanced data, and apply transfer learning.
• Leadership experience in designing large-scale data science projects, mentoring teams, and making data-driven business decisions.

Example Tasks:​

• Designing and implementing custom deep learning architectures for complex problems (e.g., image recognition, natural language understanding).
• Leading a team of data scientists in building scalable and efficient data pipelines.
• Managing and deploying machine learning models at scale for real-time or high-impact applications.
• Developing and deploying end-to-end AI systems and integrating them with business operations.

1. Beginner​

Skills:​

• Basic navigation and familiarity with the Excel interface (e.g., entering data, formatting cells, and using basic functions).
• Ability to perform basic arithmetic using formulas (SUM, AVERAGE, MIN, MAX, etc.).
• Sorting and filtering data in a spreadsheet.
• Creating simple charts and graphs (e.g., bar charts, pie charts).
• Basic formatting skills (e.g., changing fonts, adjusting column widths, and cell colors).

Example Tasks:​

• Creating a simple budget or expense tracker.
• Applying basic formatting for readability.
• Using SUM and AVERAGE to calculate totals and averages.

2. Intermediate​

Skills:​

• Proficient with more advanced formulas and functions (IF, VLOOKUP, HLOOKUP, COUNTIF, SUMIF).
• Ability to work with large datasets, including filtering, sorting, and conditional formatting.
• Experience with PivotTables for summarizing data and performing basic analysis.
• Familiarity with data validation and creating drop-down lists for controlled data entry.
• Knowledge of linking multiple sheets and workbooks together.

Example Tasks:​

• Creating a dynamic sales report using PivotTables.
• Using VLOOKUP to match data from different sheets.
• Applying conditional formatting to highlight key data points (e.g., top 10 values, color coding based on thresholds).

3. Advanced​

Skills:​

• Mastery of advanced formulas (e.g., INDEX, MATCH, ARRAY, OFFSET, INDIRECT).
• Proficient with advanced data analysis tools like PivotCharts and data consolidation across multiple sources.
• Knowledge of advanced data manipulation techniques, including data modeling and Power Query.
• Proficiency in using Excel’s data analysis tools (e.g., Solver, Goal Seek, and scenario analysis).
• Ability to create complex, dynamic dashboards with interactive elements (e.g., slicers, dynamic charts).

Example Tasks:​

• Building a comprehensive financial model or business forecasting tool.
• Using Power Query to clean and transform large datasets.
• Creating advanced dashboards with PivotCharts and dynamic visualizations.

4. Expert​

Skills:​

• Mastery of VBA (Visual Basic for Applications) for automating complex tasks and building custom Excel functions.
• Ability to design macros for automating repetitive tasks and creating user-defined functions.
• Extensive experience with Excel’s data analysis add-ins and external integrations (e.g., Power Pivot, Power BI).
• Expertise in working with large datasets, advanced statistical analysis, and complex data visualizations.
• Knowledge of collaborating with other tools and systems (e.g., integrating Excel with databases, APIs, or external applications).

Example Tasks:​

• Automating report generation with custom VBA scripts and macros.
• Designing complex financial or operational models with scenario analysis and sensitivity testing.
• Creating custom functions or add-ins to extend Excel’s native capabilities.
• Handling and analyzing data from external sources (e.g., SQL databases) and integrating it with Excel workflows.

1. Beginner​

Skills:​

• Basic understanding of Python syntax and data structures (lists, tuples, dictionaries, sets).
• Ability to write simple programs using variables, loops, conditionals, and functions.
• Understanding basic concepts like input/output, string manipulation, and basic error handling.
• Limited experience with libraries (e.g., math, random).

Example Tasks:​

• Writing a program to print Fibonacci numbers.
• Simple file handling (e.g., reading and writing text files).
• Using loops to iterate over data structures.

2. Intermediate​

Skills:​

• Deeper understanding of data structures and algorithms.
• Familiarity with object-oriented programming (OOP) principles: classes, inheritance, polymorphism, encapsulation.
• Ability to use third-party libraries and frameworks (e.g., Pandas, NumPy, Flask).
• Understanding of error handling using exceptions.
• Familiarity with modules, packages, and Python's standard library.

Example Tasks:​

• Writing a web scraper using libraries like BeautifulSoup or Scrapy.
• Creating a simple web application using Flask or Django.
• Data manipulation and analysis using Pandas and NumPy.
• Implementing algorithms like sorting or searching.

3. Advanced​

Skills:​

• Proficient in working with complex data structures (e.g., generators, iterators).
• Expert in OOP, design patterns, and advanced Python concepts (e.g., decorators, context managers).
• Understanding concurrency and parallelism (using threading, multiprocessing, async/await).
• Proficiency in performance optimization (e.g., time complexity, memory usage).
• Experience with debugging, testing (unit tests, integration tests), and version control (e.g., Git).

Example Tasks:​

• Developing a large-scale application with efficient data handling.
• Building and maintaining APIs with complex architectures.
• Writing unit tests and utilizing continuous integration (CI/CD).
• Implementing machine learning models with libraries like TensorFlow or PyTorch.

4. Expert​

Skills:​

• Mastery of Python internals, such as memory management, garbage collection, and bytecode.
• Ability to contribute to Python core development or design custom libraries and tools.
• Deep understanding of multithreading, asynchronous programming, and distributed systems.
• Familiarity with low-level programming concepts (e.g., interfacing Python with C/C++).
• Knowledge of various domains such as web development, machine learning, automation, data science, and scripting.

Example Tasks:​

• Designing complex, scalable systems and APIs for production.
• Implementing and optimizing large-scale machine learning pipelines.
• Contributing to open-source Python projects or writing custom Python extensions.

1. Beginner​

Skills:​

• Understanding basic SQL syntax and queries.
• Ability to create simple queries to retrieve data using SELECT, WHERE, and ORDER BY clauses.
• Familiarity with basic data operations like INSERT, UPDATE, and DELETE.
• Basic knowledge of filtering data with operators like =, >, <, LIKE, and IN.

Example Tasks:​

• Writing a query to select data from a table based on specific conditions.
• Sorting and filtering results using ORDER BY and WHERE.
• Inserting new rows into a table.

2. Intermediate​

Skills:​

• Proficiency with JOIN operations (INNER, LEFT, RIGHT, FULL OUTER) to combine data from multiple tables.
• Ability to group and aggregate data using GROUP BY and aggregate functions (COUNT, SUM, AVG, MAX, MIN).
• Understanding of subqueries and nested queries.
• Knowledge of database constraints (e.g., primary keys, foreign keys, unique constraints) and indexes.
• Experience with database normalization and designing relational database schemas.

Example Tasks:​

• Joining multiple tables to retrieve related data.
• Writing queries to summarize data using group functions like COUNT or SUM.
• Creating database tables and defining relationships between them.

3. Advanced​

Skills:​

• Advanced query optimization techniques to improve query performance (e.g., indexing, query plans).
• Ability to write complex stored procedures, functions, and triggers.
• Proficiency in advanced SQL features like WITH (CTE, Common Table Expressions) and window functions (e.g., ROW_NUMBER(), RANK()).
• Knowledge of database transactions, ACID properties, and handling concurrency and isolation levels.
• Proficient in database security, user roles, and permissions management.

Example Tasks:​

• Writing stored procedures and complex triggers to automate database operations.
• Optimizing slow queries by analyzing query plans and using appropriate indexing.
• Implementing transaction-safe SQL queries and managing concurrency.

4. Expert​

Skills:​

• Deep understanding of database architecture and internals (e.g., how indexes work, locking mechanisms, execution plans).
• Expertise in database design, partitioning strategies, and sharding for large-scale systems.
• Proficiency in advanced optimization techniques and tuning complex queries for performance.
• Ability to manage and configure database replication, backup, recovery, and high availability setups.
• Familiar with various database technologies (e.g., relational vs. NoSQL) and how to use SQL in different environments (e.g., cloud databases).

Example Tasks:​

• Designing and implementing highly scalable database architectures for enterprise applications.
• Performing complex query optimization and database tuning for high-performance systems.
• Configuring and maintaining database clusters and replication systems.