We are seeking an exceptional Data Engineer to design, develop, and scale data pipelines, warehouses, and streaming systems that power mission-critical analytics and AI workloads. This is a hands-on engineering role where you will work with cutting-edge technologies, tackle complex data challenges, and shape the organization's data architecture.
Core Responsibilities
Design & own robust ETL/ELT pipelines using Python and Apache Spark for batch and near real-time processing.
Architect and optimize enterprise data warehouses (Snowflake, BigQuery, Redshift, Azure Synapse) for performance and scalability.
Build data models and implement governance frameworks ensuring data quality, lineage, and compliance.
Engineer streaming data solutions using Kafka and Kinesis for real-time insights.
Collaborate cross-functionally to translate business needs into high-quality datasets and APIs.
Proactively monitor, tune, and troubleshoot pipelines for maximum reliability and cost efficiency.
5-9+ years in data engineering, with proven delivery of enterprise-scale solutions.
Advanced skills in Python, Apache Spark, and SQL optimization.
Deep expertise in at least one leading data warehouse (Snowflake, BigQuery, Redshift, Azure Synapse).
Strong knowledge of data modeling, governance, and compliance best practices.
Hands-on experience with streaming data technologies (Kafka, Kinesis).
Cloud proficiency in AWS, Azure, or GCP.
Preferred Qualifications
Experience with Airflow, dbt, or similar orchestration frameworks.
Exposure to DevOps & CI/CD in data environments.
Familiarity with ML pipelines and feature stores.
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