Software Quality Engineer

Supermicro

. Maintain and scale daily ETL/ELT pipelines using workflow or scheduling tools .

Posted 10/12/2026full-timeBade • TaiwanJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in maintaining and scaling ETL/ELT pipelines, optimizing PostgreSQL for data ingestion, and implementing data quality checks. Proficient in data processing using Python and related libraries, with strong capabilities in SQL and data warehousing.

Highest-signal resume keywords

ETL/ELT Pipeline ManagementPostgreSQL OptimizationPython Data ProcessingSQL Query WritingData Quality Assurance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python 3PostgreSQLETL/ELTSQLPandasNumpyData WarehousingSchema DesignIncremental LoadsData Processing

Soft Skills

Bilingual CommunicationCollaboration with Engineering Teams

Tools & Technologies

Workflow ToolsGitWindows ServerDockerTask Scheduling

Certifications & Qualifications

BS/MS in Computer ScienceInformation ManagementElectrical EngineeringStatisticsMathematics

Industry Keywords

Manufacturing MetricsKPI DefinitionQuality MetricsData Quality ChecksAnomaly Detection

Tech Stack

Tools & technologies

DockerETLNumpyOraclePandasPostgresPythonSQL

About the role

Key responsibilities & impact

Maintain and scale daily ETL/ELT pipelines using workflow or scheduling tools
Build extractors for manufacturing reports in Excel, CSV, email attachment, and compressed archive formats
Implement incremental loads with idempotent transforms, deduplication, checkpoints, and rerun-safe jobs
Optimize PostgreSQL batch ingestion using COPY / UPSERT for large volumes with stable memory use
Monitor scheduled production runs and handle failures, alerting, and recovery
Maintain backend data APIs and services supporting analytics dashboards and reports
Write and tune PostgreSQL queries using CTEs and window functions
Support KPI definition, trend analysis, dashboards, reporting, and ad-hoc data requests
Standardize manufacturing metrics including station yield, First Pass Yield (FPY), and repair turnaround time (TAT)
Apply basic statistics or data science methods such as anomaly detection and SPC-style quality tracking
Serve as technical contact for data exchange with contract manufacturing sites
Align report formats, column schemas, and naming standards with site engineering teams
Improve data quality in incomplete or inconsistent fields such as defect classifications and part numbers
Manage master data versioning, including BOM, and coordinate automated report collection
Investigate source-data issues including template changes, corrupted files, and encoding or formatting errors
Design and maintain warehouse layers including staging, ODS, DW, and application summary tables
Manage PostgreSQL schema changes, indexing, and routine database maintenance
Enforce role-based access control for ETL, BI, and administrative users
Maintain data quality checks, pipeline tests, and documentation
Own historical backfill procedures including backup, reset, reprocessing, and verification

Requirements

What you’ll need

2+ years of hands-on experience with Python 3
Proficiency in pandas, numpy, and related libraries for data processing and ETL
Strong SQL and relational database skills, especially PostgreSQL (or Oracle / MS SQL)
Knowledge of CTEs, window functions, UPSERT, schema design, and indexing
Experience building and operating production ETL/ELT jobs with workflow or scheduling tools
Experience with schema validation, deduplication, batch processing, and basic error handling / alerting
Experience with Git branching workflows and configuration management
Ability to discuss data requirements with engineering and operations teams in Chinese and English
BS/MS in Computer Science, Information Management, Electrical Engineering, Statistics, Mathematics, or a related technical field
Preferred: Web / API backend development and dashboard / reporting services
Preferred: Data warehousing with staging / ODS / DW layers and aggregate summary tables
Preferred: Exploratory analysis, KPI definition, and manufacturing or quality metrics (yield, FPY, TAT, SPC)
Preferred: Statistics, feature preparation, or basic machine learning
Preferred: Parsing semi-structured files such as Excel, CSV, PDF, email attachments, and archives
Preferred: Windows Server, task scheduling, or Docker
Preferred: Interest in LLM / RAG applications on internal data platforms

Benefits

Comp & perks

Equal Opportunity Employer policy and diversity commitment
Benefits are referenced via an “Our Benefits” section, but no specific benefits are provided in the posting