Lead Data Engineer

A senior data engineering leader capable of driving data modernization, transformation, governance and analytics consumption initiatives while leading teams and delivering scalable cloud-native data platforms. • Build and manage scalable ETL/ELT pipelines for structured, semi-structured, and unstructured data. • Develop batch and real-time data processing solutions using modern cloud and big data technologies. • Design and implement data models, data marts, and analytics-ready datasets for reporting, BI, AI, and ML use cases. • Drive cloud data engineering initiatives across AWS, Azure, or GCP environments. • Ensure data quality, governance, lineage, security, and compliance across the data ecosystem. • Collaborate with Business, Data Architects, Data Scientists, and BI teams to deliver business value through data products. • Lead modernization of legacy data warehouses and support data lakehouse architectures and platform transformations. • Establish engineering best practices, CI/CD pipelines, Infrastructure as Code (IaC), and automation standards. • Mentor and lead Data Engineers while conducting architecture reviews and technical governance. • Optimize platform performance, scalability, reliability, and cloud costs. Core Technical Skills • SQL, Python, PySpark, Spark • Data Modeling (Star Schema, Snowflake, Data Vault) • Airflow, DataStage, Informatica, Talend, DBT • BigQuery, Snowflake, Synapse, Redshift • Kafka, Pub/Sub, Event Hub • Git, CI/CD, Terraform, Docker, Kubernetes • Cloud Platforms: AWS, Azure, GCP Preferred Skills • Data Lakehouse (Databricks, Delta Lake) • Master Data Management (MDM) • Data Mesh/Data Product Architecture • AI/ML & GenAI Data Platforms • BFSI, Insurance, Retail, or Healthcare domain experience.