Lead Data Engineer

Lead Data Engineer (Remote for India only)

• Strong hands-on expertise in SQL, DBT and Python for data processing and transformation.
• Expertise in Azure data services (e.g., Azure Data Factory, Synapse, Event Hub) and orchestration tools.
• Strong experience with Snowflake – including schema design, performance tuning, and security model.
• Good understanding of DBT for transformation layer and modular pipeline design.
• Hands-on with Git and version control practices – branching, pull requests, code reviews.
• Understanding of DevOps/DataOps principles – CI/CD for data pipelines, testing, monitoring.
• Knowledge of data modeling techniques – Star schema, Data Vault, Normalization/Denormalization.
• Experience with real-time data processing architectures is a strong plus.
• Proven leadership experience – should be able to mentor team members, take ownership, make design decisions independently.
• Strong sense of ownership, accountability, and solution-oriented mindset.
• Ability to handle ambiguity and work independently with minimal supervision.
• Clear and confident communication (written and verbal) – must be able to represent design and architecture decisions.
• Lead the design and development of data pipelines (batch and real-time) using modern cloud-native technologies (Azure, Snowflake, DBT, Python).
• Translate business and data requirements into scalable data integration designs.
• Guide and review development work across data engineering team members (onshore and offshore).
• Define and enforce best practices for coding, testing, version control, CI/CD, data quality, and pipeline monitoring.
• Collaborate with data analysts, architects, and business stakeholders to ensure data solutions are aligned with business goals.
• Own and drive end-to-end data engineering workstreams – from design to production deployment and support.
• Provide architectural and technical guidance on platform setup, performance tuning, cost optimization, and data security.
• Drive data engineering standards and reusable patterns across projects to ensure scalability, maintainability, and reusability of code and data assets.
• Define and oversee data quality frameworks to proactively detect, report, and resolve data issues across ingestion, transformation, and consumption layers.
• Act as a technical go-to team member for complex design, performance, or integration issues across multiple teams and tools (e.g., DBT + Snowflake + Azure pipelines).
• Contribute to hand on development as well for the ned to end integration pipelines and workflows.
• Document using Excel, Word, or tools like Confluence.