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Mid-level generalist title with broad Spark/Kafka/DBT requirements creates a high competition pool.
Specialized data-platform technologies required but skills remain transferable across industries.
Mandatory 4+ years and many specific technologies (Iceberg, Spark, Kafka, DBT, Snowflake) increases filter strictness.
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Build, optimize, and maintain scalable data ingestion pipelines for diverse data types (relational, semi-structured, unstructured) primarily on AWS.
Design and optimize lakehouse architectures using Apache Iceberg, including table design, partitioning, and performance tuning.
Implement and manage real-time streaming solutions with Kafka, and orchestrate workflows using DBT and Airflow with strong focus on data quality, observability, and performance.
Bachelor’s degree in Computer Science, Engineering, or related technical field.
4+ years of experience in designing, building, and maintaining data pipelines.
Hands-on expertise with AWS data services (Glue, EMR, Lambda, Step Functions, S3), Apache Spark, Apache Iceberg, Kafka, DBT, Airflow, and Snowflake.
Strong programming skills in SQL and Python with a deep understanding of data quality frameworks and real-time streaming pipelines.
Experience working extensively with modern lakehouse architectures and distributed systems design, emphasizing efficient data processing and versioning.
Proficiency in developing robust, automated ETL/ELT pipelines leveraging AI/LLM-based tools for development acceleration and quality validation.
Track record of collaborating closely with cross-functional teams (data engineering, analytics, reporting) to improve pipeline reliability, observability, and scalability.