Data Engineer

Data Engineering & Architecture

• Design, develop, and maintain scalable, high-performance data pipelines
• Work extensively with Azure Data Factory and Microsoft Fabric
• Build robust ETL/ELT frameworks using Python
• Design and optimize Lakehouse / Data Warehouse architectures
• Handle large-scale datasets efficiently (high volume and throughput)
• Write and optimize complex SQL queries for performance and reliability
• Integrate data from multiple sources including APIs, transactional systems, and external platforms

Leadership & Delivery (Hands-on)

• Lead and mentor a team of data engineers while remaining actively involved in coding and solution design
• Perform hands-on development for critical pipelines, complex transformations, and performance optimisation.
• Conduct code reviews and enforce best practices, design patterns, and coding standards
• Act as the technical owner for data engineering deliverables

Quality, Performance & Reliability

• Implement data quality checks, validations, and monitoring
• Optimize pipelines for performance, scalability, and cost
• Ensure reliability, fault tolerance, and error handling in production systems
• Follow data security, access control, and compliance best practices
• Lead troubleshooting, root-cause analysis, and production issue resolution

Collaboration & Continuous Improvement

• Work closely with BI, analytics, product, and business teams
• Translate business requirements into scalable technical solutions
• Stay up to date with modern data engineering tools, technologies, and techniques
• Proactively suggest architectural and process improvement

Requirements

• 3-6 years of experience in the Data Engineering field.
• Strong hands-on experience in Python for data engineering, including building and maintaining production-grade, large-scale data pipelines
• Advanced experience with Azure Data Factory and Azure-based data platforms for orchestration, integration, and scalable data processing
• Working experience with Microsoft Fabric, including Lakehouse and data engineering workloads, along with a strong understanding of ETL/ELT and data warehousing concepts
• Expert-level SQL skills covering complex query development, optimization, indexing, and partitioning for high-performance systems
• Proven experience handling large-volume, high-throughput data and distributed processing environment.
• Experience with analytics and visualization platforms such as Power BI
• Knowledge of Delta Lake, Spark, and distributed data processing frameworks
• Experience implementing CI/CD practices for data pipelines and data engineering workflows
• Exposure to data governance, lineage, metadata management, and compliance-driven environments such as fintech or high-transaction systems
• Hands-on leadership mindset with strong ownership and accountability for outcomes
• Ability to mentor, guide, and grow junior engineers while leading by example
• Clear and effective communication with technical and non-technical stakeholders
• Strong problem-solving, analytical reasoning, and decision-making skills

Benefits

• Working hours: 10:00 AM – 7:00 PM
• Working days: 5 days a week (plus 1st & 3rd Saturdays working)
• Medical Insurance coverage for employees
• Provident Fund (PF) facility
• Quarterly parties and yearly outings/trips for team bonding
• Regular check-ins with leadership for growth and feedback
• Recognition awards to celebrate high performance
• Fun activities and team engagement sessions throughout the year