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We are seeking a Principal Data Platform Engineer to define, evolve, and scale our enterprise data platform. This role is data-first and architecture-driven, with hands-on impact across ETL/ELT, big data, streaming, and cloud data platforms. You will act as a technical authority, shaping long-term platform direction, setting engineering standards, and mentoring senior engineers across teams.
Job Responsibility:
Own and define data platform architecture, standards, and long-term technical roadmap
Design and oversee scalable ETL/ELT pipelines using Python across multiple data domains
Establish data ingestion and data access APIs using Python and FastAPI for platform consistency and reuse
Lead design and optimization of batch and streaming pipelines using Spark and Apache Kafka
Drive architecture decisions for relational databases (MySQL, Oracle), cloud data warehouses (AWS Redshift), and NoSQL systems (Elasticsearch)
Guide large-scale data processing using Hive, Trino, and Hadoop distributed computing
Define standards for object and file storage integrations (AWS S3, Dell ECS, SFTP)
Enable data quality, lineage, governance, and reliability at platform scale
Support analytics and BI enablement (Power BI, Spotfire) through well-modeled datasets
Contribute to lightweight internal UIs using React for data observability, configuration, or platform tooling (custom, not product UI)
Mentor senior and junior engineers
influence architecture across teams
Requirements:
10+ years of experience in data engineering, data platform, or large-scale distributed data systems
Demonstrated ownership of data platform architecture in complex environments
Deep expertise in Python, SQL, and ETL/ELT design
Strong hands-on experience with distributed data systems, including: Big data: Spark, Hive, Trino, Hadoop