【 Backend-end Engineer (Data)】

【 Job Summary】

As a Python Engineer at Mlytics, you will play a pivotal role in designing, developing, and maintaining data solutions that power our data-driven initiatives. Leveraging your expertise in Python programming, database management, and cloud architecture, you will be responsible for building robust data pipelines, optimizing data processes, and developing RESTful APIs to enable seamless data access and integration. Collaborating closely with analysts, front-end developer and other stakeholders, you will translate data requirements into scalable and efficient solutions, ensuring data quality, integrity, and security throughout the data lifecycle. With a focus on continuous learning and improvement, you will stay abreast of the latest advancements in Python programming, database technologies, and cloud platforms, contributing to the enhancement of our data architecture and infrastructure.


【Responsibilities】:
◉ Design and implement cloud-based data solutions using platforms such as AWS, GCP, or Azure, leveraging services like AWS S3, GCP BigQuery, or Azure SQL Database.
◉ Develop and maintain data pipelines and ETL processes, ensuring efficient extraction, transformation, and loading of data from various sources into databases.
◉ Design and optimize database schemas and queries to efficiently store and retrieve data, leveraging SQL and database optimization techniques.
◉ Develop RESTful APIs using Python frameworks such as FastAPI or Flask to expose data and services to internal systems and external clients.
◉ Troubleshoot and debug data-related issues.
◉ Implement best practices for Python development, including code optimization, testing, documentation, and version control.
◉ Stay up-to-date with advancements in Python programming, database technologies, and API development frameworks, incorporating new tools and techniques as appropriate to enhance productivity and efficiency.


【OKRs】 - To be further discussed after onboard
● Objective#1: Ensure Data Quality and Integrity
◉ Implement data validation checks, reducing data entry errors by 25% within the first two quarters.
◉ Reduce data anomalies by 15% through the implementation of automated data cleansing routines within data pipelines.
◉ Achieve a 95% data accuracy rate as measured by data quality metrics established in collaboration with product owner and analysts.

● Objective#2: Enhance Data Pipeline Efficiency and Reliability
◉ Maintain a data pipeline uptime of 99% by implementing proactive monitoring and troubleshooting procedures for pipeline issues.
◉ Establish comprehensive performance monitoring for data pipelines, including metrics such as throughput, latency, and error rates, and ensure proactive notification of performance deviations, achieving a 90% accuracy in detecting performance issues within the first three months.

● Objective #3: Strengthen API Development and Integration
◉ Develop and deploy RESTful APIs for accessing key data sources, achieving a minimum of 95% API uptime within the first three months.
◉ Successfully integrate APIs with frontend applications, achieving a 90% satisfaction rating from frontend developers based on ease of integration and documentation clarity.
◉ Establish a robust testing framework for APIs, including unit tests, integration tests, and end-to-end tests, achieving 70% code coverage and reducing the number of post-deployment issues by 20% within the first six months.

【Interview Process】
◉ 1st round - Online google meet interview (1 hour)
Panel Interview with Product Managers and Backend Engineer
General Q & A focus on work and technical backgrounds, understanding, knowledge.

◉ 2nd round - In person onsite interview (2 hours)
1st hour - Technical Assessment
2nd hour - People Operations Interview