About the Company

A leading provider of AI-powered spend management and financial risk mitigation solutions for large enterprises. The platform continuously monitors and analyzes spend transactions to detect fraud, waste, and misuse helping organizations prevent financial loss, optimize spend, and strengthen compliance controls.

📌 We're Looking For

A passionate AWS Data Analytics Engineer who thrives in cloud-native environments and loves turning raw data into powerful, scalable analytics. If you have deep expertise in AWS, Databricks, Python, and Spark SQL and enjoy both the engineering and strategic side of data this role is for you. You'll be part of a high-impact team building high-performance data pipelines and analytics solutions, working closely with product and engineering to drive real business decisions. We need someone who takes ownership, moves fast, and isn't afraid to lead.

🏢 About the Company

A leading provider of AI-powered spend management and financial risk mitigation solutions for large enterprises. The platform continuously monitors and analyzes spend transactions to detect fraud, waste, and misuse — helping organizations prevent financial loss, optimize spend, and strengthen compliance controls.

📌 We're Looking For

A passionate Data Analytics Engineer who thrives in cloud-native environments and loves turning raw data into powerful, scalable analytics. If you have deep expertise in AWS, Databricks, Python, and Spark SQL and enjoy both the engineering and strategic side of data — this role is for you. You'll be part of a high-impact migration team moving clients from legacy environments to AWS, building lakehouse-style data solutions on Delta Lake and governed analytics through Unity Catalog. We need someone who takes ownership, moves fast, and isn't afraid to lead.

🎯 Responsibilities

• Lead, mentor, and develop a growing team of analytics engineers
• Collaborate with product management to define analytics, assess feasibility, and translate requirements into data designs and pipelines
• Design, build, and deploy scalable data pipelines on Databricks using PySpark, Spark SQL, and Delta Lake patterns
• Build and optimize data transformations using Spark SQL and Python, maintaining SQL as code alongside PySpark
• Model and document data for Unity Catalog consumers across bronze/silver/gold layers
• Operate jobs responsibly — tune for performance and cost, handle schema evolution, data quality checks, and improve observability
• Work across the full software development lifecycle with cross-functional teams including code review, testing, and release discipline
• Oversee documentation, runbooks, and training for product deliverables and features
• Act as a trusted technical partner and communicate technical capabilities effectively to stakeholders
• Define and track metrics to evaluate whether analytics solutions are accurate, timely, and fit for purpose
• Contribute to AI-powered product features integrating large language models and retrieval-based systems over enterprise data

✅ Requirements

• Bachelor's degree in Computer Science, Information Systems, Data Science, or related field
• 4+ years of experience in data engineering, analytics engineering, or software development
• Experience working with AWS cloud services relevant to data platforms
• Hands-on experience with ETL/ELT processes — transforming, mapping, cleansing, and preparing large datasets
• Strong proficiency in Python and PySpark for distributed transforms and orchestrated jobs
• Advanced proficiency in Spark SQL — comfortable maintaining and evolving SQL as code alongside Python
• Hands-on Databricks experience — Jobs/Workflows, notebooks, job parameters, and cluster configuration in a multi-environment setup
• Delta Lake experience — tables, incremental patterns, schema evolution, and performance tuning
• Unity Catalog experience — catalogs, schemas, permissions, and securable data objects in a shared platform
• Strong analytical skills and track record of troubleshooting pipeline failures and data anomalies
• Advanced English proficiency — written and spoken

⭐ Nice to Have

• Databricks Asset Bundles and CI/CD exposure
• MLflow on Unity Catalog or Databricks Feature Store
• Experience designing end-to-end analytics solutions that support productized reporting
• Background in data modeling, warehousing, and lakehouse architecture
• Familiarity with big data and distributed processing technologies
• Experience with ERP systems (SAP, Oracle, PeopleSoft)
• PostgreSQL alongside Spark in hybrid pipelines
• Experience in a product-oriented software development environment