Design, build, and maintain scalable batch and real-time data pipelines
Architect and implement modern Lakehouse solutions using technologies such as Delta Lake or Apache Iceberg
Develop and manage ETL/ELT workflows using tools like Airflow, dbt, or Prefect
Implement robust data quality, governance, lineage, and monitoring frameworks
Design optimized data models to support analytics, business intelligence, and ML workloads
Improve platform scalability, reliability, performance, and cost efficiency
Collaborate with Analytics, Product, and AI/ML teams to enable data-driven solutions
Lead and mentor a team of data engineers while driving engineering best practices and standards
Build and maintain CI/CD pipelines and infrastructure automation for data platforms
Ensure platform reliability, observability, and operational excellence across the data ecosystem

7–10 years of experience in Data Engineering
Strong expertise in SQL, Python, and Spark (PySpark /Scala)
Hands-on experience with modern cloud data platforms such as: Snowflake, Databricks, BigQuery, Amazon Redshift
Strong understanding of data modeling methodologies including Kimball and Data Vault
Experience working with streaming and real-time data systems such as Kafka, Flink, or similar technologies
Familiarity with infrastructure-as-code and DevOps practices using Terraform and CI/CD pipelines
Hands-on experience with cloud platforms including AWS, GCP, or Azure
Strong understanding of scalable distributed data systems and modern data architecture patterns
Advanced degree in Computer Science, Data Engineering, or a related field preferred.

Lead Data Engineer

Key skills