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# Data Engineer

- **Company:** After School Matters, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Dimensional Modeling, Fault Tolerance, Python (Programming Language), SQL Databases, Apache Spark, Apache Kafka, Virtual Agents, Data Delivery, Data Pipelines, Databricks
- **Published:** August 20, 2026
- **Apply:** https://nitkatechnologies.applytojob.com/apply/7GXC2f91zM/Data-Engineer?source=GS

## About the Role

* 5+ years of Data engineering experience;
 * Advanced proficiency in Python, Spark, and SQL;
 * Strong experience with AWS and Databricks;
 * Familiarity with Kafka and modern Data stack tools (DBT, Airflow or equivalent);
 * Deep understanding of dimensional modeling (star/snowflake schemas), medallion architecture, data warehouse design principles;
 * AI fluency: able to structure problems to leverage AI assistance and design pipelines that assume AI-assisted development;
 * Knowledge of system topologies (lambda, kappa, lakehouse);
 * Strong spoken English (Intermediate level and higher).

## Description

* Lead discovery and requirements-gathering sessions with business stakeholders; document source schemas and define analytics-ready data model specifications;
 * Build, optimize and maintain end-to-end data pipelines ETL/ELT frameworks with reusable dbt macros, incremental models, and snapshot patterns to ensure fault-tolerant, scalable data delivery;
 * Design and implement data models following medallion architecture patterns (bronze/silver/gold), creating foundational tables, star schemas, and reporting marts with defined SLAs and data quality contracts;
 * Tune Spark jobs and pipeline components to meet SLA requirements by optimizing partitions, reducing shuffle, and implementing efficient transformation patterns;
 * Implement automated data quality checks using frameworks like Great Expectations, establishing quality contracts across pipeline layers;
 * Diagnose complex, multi-system pipeline issues-profiling Spark UI stages, tracing Kafka consumer lag, identifying dbt dependency failures-and design mitigations proactively;
 * Create and maintain technical documentation, data model diagrams, and operational runbooks for production support


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