Key Responsibilities
• Design, develop, and maintain scalable, production-grade ETL/ELT data pipelines
using modern orchestration frameworks including Apache Airflow, Apache Spark,
Kafka, and MuleSoft
• Build data ingestion solutions from diverse enterprise source systems including ERP
platforms (SAP, Oracle), CRM platforms (Salesforce, Workday), and cloud data
warehouses, extracting data via APIs, connectors, and direct database access
• Develop and maintain analytical content including business metrics, KPIs, and
dashboards that drive measurable enterprise outcomes
• Implement data modelling best practices across relational, dimensional, and cloud
warehouse schemas to support downstream analytics consumption
• Build and maintain data quality frameworks, validation rules, and
observability/monitoring pipelines to ensure high-quality, trustworthy data across all
product applications
• Optimize data processing workflows for performance, reliability, and cost-efficiency
in cloud-native environments (AWS, Azure, GCP)
• Collaborate with BI Engineers, Data Modellers, Product Managers, and UX Designers
to ensure seamless data flow from source systems through transformation layers to
analytical dashboards and AI-ready datasets
• Write clean, reusable, and well-tested code following software engineering best
practices including code reviews, unit testing, and CI/CD
• Work with product owners to understand detailed requirements and own code from
design through implementation, test automation, and high-quality delivery
• Implement software that is extensible and allows customers to customize
functionality to meet their specific needs
• Integrate AI tools and AI-driven approaches actively into engineering workflows,
pipeline development, and problem-solving — not just as a tool user, but as a critical
thinker about where AI adds value
Required Qualifications
Experience & Core Engineering
• 5–12 years of proven track record in building production-grade data pipelines and
ETL/ELT solutions at enterprise scale
• Strong proficiency in Python for data engineering — including PySpark, pandas,
SQLAlchemy, and data processing libraries
• Advanced SQL skills: complex joins, window functions, query optimization,
performance tuning, and analytical query design
• Hands-on experience with cloud platforms (AWS, Azure, or GCP) and cloud-native
data services
Data Infrastructure & Tooling
• Proficiency with orchestration frameworks: Apache Airflow, Apache Spark,
Kafka, MuleSoft, or equivalent
• Hands-on experience with modern data warehouses and lakehouses:
Snowflake, Databricks, BigQuery, or Redshift — including data modelling,
optimization, and cost management
• Working knowledge of ERP/CRM system integrations: SAP, Oracle, Salesforce,
Workday — extracting data via REST APIs, database connectors, or certified adapters
Analytics & Data Modelling
• Experience building analytical content: metrics, KPIs, dashboards, and data
products that directly serve business decision-making
• Solid understanding of data quality principles, testing frameworks, and data
validation methodologies
• Familiarity with any analytics and data visualization tools such as Tableau,
Looker, or Power BI — and how they integrate into data workflow solutions
AI & Collaboration
• Demonstrated experience leveraging or critically thinking about AI
integration in data engineering workflows, decision-making, and problem-solving
— including using AI-powered tools, automating workflows, or evaluating AI's impact
on data pipelines
• Strong communication, collaboration, and technical skills to work effectively
within cross-functional product engineering teams
Nice-to-Have Qualifications
• Active use of AI-powered productivity tools such as Cursor, Windsurf, Claude, or
GitHub Copilot for accelerated development
• Hands-on Java experience and working knowledge of enterprise application design
patterns
• Experience working with ServiceNow platform or similar enterprise workflow
platforms
• Experience with containerization (Docker, Kubernetes) and modern CI/CD pipeline
practices
