Hiring: Python Developer (Big Data & SAS Migration Engineer)
Company: Premier Enterprise Financial Services & Technology Consulting Group
Location: Singapore, Singapore (On-site / Central Business District)
Employment Type: Full-time
Seniority Level: Mid-Senior level
Estimated Salary: SGD 6,500 - SGD 9,500 per month (Based on localized market compensation for specialized data migration and PySpark big data engineers within Singapore's banking and tech sectors)
Industry: Information Technology & Services / Banking / Financial Services
Company Overview
We are a leading enterprise technology integration and consulting firm driving large-scale digital transformations for major financial institutions and multinational corporations across the Asia-Pacific region.
Specializing in legacy infrastructure modernization and cloud-native architecture, our teams process massive volumes of complex data to enable predictive machine learning and high-frequency analytical modeling. We maintain an engineering-first culture that prioritizes architectural optimization, scalable automation, and robust data compliance.
Eligibility Note: This position is strictly open to Singapore Citizens and Singapore Permanent Residents (PR) to comply with localized project security clearings and institutional banking access frameworks.
Key Responsibilities
Big Data Pipeline Architecture: Design, develop, and maintain high-throughput distributed computing applications using PySpark, Hive, and the broader Hadoop ecosystem to process complex multi-terabyte datasets.
Legacy System Migration: Analyze, re-engineer, and systematically migrate highly complex legacy SAS analytics scripts, data pipelines, and mathematical macros into modern, optimized, and modular Python codebases.
Query & Execution Optimization: Profile and optimize slow-running PySpark jobs and Hive queries, addressing memory management, partitions, and shuffle bottlenecks to maximize distributed processing performance.
Collaborative Data Modeling: Partner closely with internal Data Scientists, Risk Analysts, and Quant teams to engineer robust data models, perform granular data wrangling, and build out production-ready analytical pipelines.
System Troubleshooting & Debugging: Perform root-cause analysis on distributed processing failures, troubleshooting pipeline data degradation, latency issues, and cluster resource constraints.
Technical Documentation: Author detailed mapping blueprints and system documentation tracking the lineage, transformations, and logic adjustments made during the legacy SAS-to-Python migration phase.
Experience & Qualifications
- Proven professional background operating as a Big Data Engineer, Data Engineer, or specialized Backend Developer with strong distributed computing experience.
- Hands-on commercial background executing end-to-end data migration initiatives, specifically converting enterprise SAS architecture into open-source Python stacks.
- Deep experience navigating advanced data modeling, complex data wrangling methodologies, and exploratory data analysis.
- Exceptional analytical and problem-solving skills, with a track record of handling intricate legacy code logic with high autonomy.
- Excellent collaborative communication traits, comfortable guiding multidisciplinary development units through large-scale structural code migrations.
- Academic Foundation: A Bachelor’s degree in Computer Science, Data Science, Information Systems, or a deeply relevant quantitative discipline.
Technical Skills Required
Python & Core Big Data Stack: Advanced production mastery of Python, alongside deep practical framework expertise in PySpark, Hive, Hadoop MapReduce, and HDFS ecosystems.
Legacy Ecosystem Competency: Solid reading, writing, and debugging fluency in SAS (including Base SAS, SAS/STAT, and SAS Macros) to unpack enterprise analytics configurations accurately.
Cloud Infrastructure Platforms: Practical familiarity deploying and monitoring data workflows across modern cloud computing environments (e.g., AWS EMR, Azure HDInsight, or GCP Dataproc).
Database & Data Modeling: Expert-level command of SQL, structural schema design, data warehousing methodologies, and optimized data lake storage formats (such as Parquet or ORC).
How to Apply
To spearhead this critical big data modernization initiative, please upload your core software engineering portfolio, detailed GitHub repositories, and updated resume highlighting your past data pipeline or migration projects through our enterprise recruitment system.