Technical Skills & Competencies
Minimum 10 years of experience in Data Engineering or related roles
At least 2+ years in a Technical Lead (Data) capacity
Note: This is not a management position — 100% hands-on capability required
Proficiency in Python and SQL is essential, with proven experience in developing scalable data solutions
Strong skills in writing clean, testable, and production-grade code for data transformation and orchestration
Experience with version control systems like Git and collaborative development practices
Familiarity with Scala or Java is a plus, particularly for big data processing frameworks
Proven experience in designing and implementing enterprise-grade data lake solutions
Hands-on experience with Cloud Data Engineering (AWS/GCP), Databricks, Delta Lake, and lakehouse architectures
Strong understanding of data warehouse concepts, including star and snowflake schemas
Expertise in query performance tuning, data partitioning, compression, and storage optimization
Understanding of data governance, access control, and security frameworks
Familiarity with medallion architecture or equivalent multi-tier data processing frameworks
Experience with real-time data streaming technologies such as Apache Kafka or AWS Kinesis
2+ years of technical leadership experience
Demonstrated ability to mentor junior engineers and foster team capability growth
Excellent communication and collaboration skills across cross-functional teams
Strong problem-solving abilities, business acumen, and analytical thinking
Ability to manage multiple priorities in dynamic, fast-paced environments
Customer-focused mindset with strong stakeholder engagement skills
Experience in Agile methodologies and leading sprint planning and retrospectives
Expertise in data pipeline architectures, data integration patterns, and orchestration tools
Experience with data validation, testing frameworks, and data quality management
Knowledge of data privacy, compliance, and security best practices
Experience in connecting data pipelines to visualization layers for real-time analytics
Understanding of semantic layer design and self-service data modeling
Experience in end-to-end orchestration of pipelines from source systems to reporting tools
Proficiency in data modeling techniques and database design principles
Relevant cloud certifications (e.g., AWS Certified Data Analytics or Google Cloud Professional Data Engineer)
Understanding of industry-specific data regulations (e.g., GDPR, CCPA, HIPAA) as applicable
Knowledge of emerging trends in data engineering, such as data mesh and data fabric architectures
Additional Information