Data Engineer

•    Design, build, and maintain scalable, fault-tolerant data pipelines and ETL/ELT processes for structured, semi-structured, and unstructured data.
•    Own end-to-end pipeline orchestration - scheduling, dependency management, retries, SLAs, and observability - using a data pipeline orchestrator.
•    Build and manage lakehouse data assets on Apache Iceberg, including partitioning, schema evolution, time-travel, and table maintenance (compaction, snapshot expiry).
•    Develop and operate large-scale data processing jobs using Apache Spark, including AWS Glue Spark jobs.
•    Model, load, and optimize data in Snowflake, and govern data assets using data catalogs (AWS Glue Data Catalog and Snowflake Horizon).
•    Architect and implement data solutions on AWS using native services (S3, Glue, EMR, Lambda, Athena, Kinesis, Redshift, IAM).
•    Ensure data quality, integrity, lineage, and security across all systems and environments.
•    Integrate data from diverse sources including APIs, relational databases, streaming feeds, and third-party tools.
•    Monitor, troubleshoot, and tune pipelines for performance, reliability, and cost efficiency.
•    Contribute to platform architecture, design reviews, code reviews, and engineering best practices.
•    Mentor junior engineers and help drive standards across the data engineering function.