Data Engineer

Description

We are looking for a Data Engineer to support education-focused data initiatives through a Contract engagement based in Carmichael, California. This role will design and enhance reliable data pipelines that turn complex source information into trusted, well-structured datasets for reporting, analysis, and future advanced use cases. The ideal candidate brings strong engineering judgment, clear communication skills, and hands-on experience building scalable solutions in cloud-based environments.

Responsibilities:
Build and maintain multi-stage data pipelines that organize raw, refined, and curated data for downstream business and analytics use.
Develop incremental processing workflows that efficiently capture updates and propagate changes across datasets with accuracy and consistency.
Create approaches for matching and unifying records across separate platform instances where identifiers may overlap or conflict.
Establish data lineage, traceability, and audit controls so datasets can be validated and reviewed with confidence.
Implement and manage workflow orchestration for scheduled and event-driven pipeline execution using cloud-native or equivalent automation tools.
Write production-quality pipeline code in Python and TypeScript to support ingestion, transformation, and delivery of data assets.
Leverage AWS services such as Lambda and S3 to build resilient, scalable components for data processing workloads.
Partner with technical and non-technical stakeholders to explain design decisions, clarify tradeoffs, and align solutions with operational needs.
Prepare data models and pipeline outputs that can support future AI and machine learning applications, including training and inference workflows.

Requirements

5+ years of experience in data engineering, ETL development, or closely related data platform work.
Strong command of Python for pipeline development; familiarity with TypeScript in engineering implementations is also expected.
Hands-on experience with modern data architecture patterns, including layered pipeline design and incremental data processing.
Proficiency with AWS-based data solutions, including services such as Lambda and S3, in production environments.
Working knowledge of orchestration frameworks such as Step Functions, Airflow, or comparable workflow management tools.
Experience with big data and streaming technologies such as Apache Spark, Apache Hadoop, and Apache Kafka.
Understanding of data governance practices including lineage, auditability, and reliable change tracking across systems.
Ability to communicate complex technical topics clearly to both technical teams and business stakeholders.

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