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.