About the role
Senior Data Engineer responsible for architecting and leading the design of enterprise data platforms, data pipelines, and data infrastructure. This role combines technical expertise with mentorship, driving technical strategy and best practices while partnering with stakeholders to translate complex business requirements into scalable data solutions. Responsibilities
Architect and design enterprise-scale data platforms and pipeline solutions using Spark, Azure Databricks, and related technologies. Build and optimize data models and dimensional schemas for complex analytics and AI/MLuse-cases. Lead technical design reviews and mentor junior data engineers on best practices and architectural patterns. Establish data quality, governance, and metadata management frameworks across the platform. Collaborate with stakeholders to define data requirements and translate them into technical solutions. Drive optimization initiatives for data pipeline performance, cost, and reliability. Participate in hiring and team building for the data engineering function. Contribute to architectural decisions and long-term platform strategy. Troubleshoot complex data pipeline failures and implement robust monitoring and alerting solutions.
Minimum Qualifications
5+ years experience in data engineering, analytics engineering, or related field. Expert-level SQL and experience with modern data warehouses (Snowflake, BigQuery, Redshift, etc.). Deep experience designing and maintaining large-scale data pipelines using Spark, Airflow, or similar orchestration tools. Strong proficiency in Python or Scala for complex data processing. Advanced understanding of data modeling, dimensional design, data warehousing concepts. Experience with cloud platforms (AWS, GCP, or Azure) at scale. Proven ability to mentor and guide junior engineers.
Preferred Qualifications
Experience architecting data platforms from the ground up. Experience with Databricks, Delta Lake, and lakehouse architectures. Expertise in real-time data streaming and event-driven architectures (Kafka, Kinesis). Knowledge of data governance, data lineage, and metadata management systems. Experience with ML infrastructure and feature stores. Background in regulatory-heavy industries or complex compliance requirements. Experience with infrastructure-as-code and DataOps practices.
Originally posted on Himalayas