Role Description

The Data Engineer is responsible for architecting, building, and optimizing centralized data infrastructure (Data Warehouse / Data Lake) to empower business intelligence, operational reporting, and data-driven executive decision-making. This role owns end-to-end data integration pipelines (ETL/ELT, CDC, Batch & Real-time) across enterprise operational platforms (SAP ERP, DMS, WMS, TMS, OCS/CRM, Logs, and 3rd-party services), guaranteeing data consistency, high availability, cost efficiency, and query performance.

• Data Pipeline Architecture & Engineering: Design, construct, and optimize automated ETL/ELT pipelines ingesting heterogeneous data sources (core enterprise systems, OCS, DMS, CRM, SAP S/4HANA, WMS, TMS, Web/App Logs, 3rd-party APIs) into the centralized Data Warehouse.
• Engineer enterprise-grade data integration solutions, Change Data Capture (CDC) pipelines, and high-throughput Batch and Real-time / Near Real-time processing flows.
• Data Modeling & Architecture: Design and maintain robust data models on Data Warehouse platforms (Star Schema, Snowflake Schema) optimized for high-performance querying and analytical reporting.
• Structure domain-specific Data Marts and analytical layers aligned with operational and strategic management needs.
• DataOps & Data Quality Governance: Implement CI/CD best practices for data codebase management; build automated data testing and Data Quality checks to proactively detect and alert data anomalies.
• Monitor pipeline reliability, execution SLAs, latency, and error rates to guarantee continuous system uptime.
• Performance Tuning & Cloud Cost Optimization: Profile, troubleshoot, and optimize complex SQL queries; implement efficient partitioning, clustering, and data compression strategies to curtail cloud storage/compute expenditures.
• Cross-Functional Collaboration & Project Management: Partner closely with Data Analysts, Business Analysts, and Product Teams to translate business requirements into clean, scalable data datasets and marts. Manage milestones for Data Warehouse enhancements, pipeline re-architecting, and BI reporting ecosystem initiatives.

Qualifications

Education: Bachelor’s degree in Computer Science, Information Technology, Software Engineering, Management Information Systems (MIS), or related fields.

Experience: Minimum 2–4 years of progressive experience as a Data Engineer, DWH Developer, or ETL Engineer.

Technical Qualifications:

• Strong programming proficiency in Python or Java / Scala.
• Expert-level SQL proficiency (advanced data manipulation, analytical functions, indexing, and execution plan optimization).
• Direct hands-on experience or professional certification in modern Cloud Data Warehouses: Google BigQuery, Snowflake, AWS Redshift, or Databricks.
• Practical experience with Change Data Capture (CDC) or equivalent real-time event streaming paradigms.
• Proven competence in workflow orchestration and transformation tools: Apache Airflow and dbt (Data Build Tool).
• Working knowledge of RESTful APIs, JSON/XML data serialization, file-based data ingestion, and enterprise integration patterns.
• Proficient in Linux/Unix environments, Shell scripting, and task automation.
• Comprehensive understanding of Data Lake, Data Warehouse, Lakehouse paradigms, and modern ETL/ELT architectures