Senior Data Engineer
<p><strong>Key Responsibilities</strong></p><ul><li>Design, implement, and own ETL/ELT pipelines—both batch and real‑time—using best-in-class tools and frameworks.</li><li>Architect and maintain scalable data platforms: data lakes, warehouses, and modern lakehouses.</li><li>Drive adoption of event-driven systems, ensuring schema evolution, idempotency, and end-to-end pipeline reliability.</li><li>Leverage AI-powered tooling and automation to optimize operations and reduce manual overhead.</li><li>Implement robust DataOps practices, including CI/CD, version control (Git), testing, and observability.</li><li>Collaborate with platform and AI teams to build AI‑ready, trustworthy data systems with lineage, governance, and cost-awareness.</li><li>Monitor and optimize performance and cost across cloud services</li></ul><p><br></p><p><br></p>
<p><strong>Technical Proficiency</strong></p><ul><li>Programming & SQL: Strong Python (PySpark) and advanced SQL skills.</li><li>Big Data Frameworks: Proficient with Apache Spark (Databricks, EMR) and capable of streaming with Kafka, Flink, or Kinesis.</li><li>Pipeline Orchestration: Hands-on with tools like Apache Airflow, Prefect, or Dagster.</li><li>Cloud Platforms: Deep expertise in AWS, GCP, or Azure—covering services like S3/BigQuery/Synapse, Lambda/Dataflow/Data Factory, Glue/Snowflake.</li><li>Data Modeling & Governance: Skilled in dimensional modeling, data contracts, quality checks, and compliance.</li><li>DevOps & Automation: Experience with CI/CD frameworks (GitHub Actions/Azure DevOps), IAC (Terraform), and unit/integration testing.</li></ul><p><br></p><p><br></p>
<h3 class="rh-display-3--rich-text">Technology Doesn't Change the World, People Do.<sup>®</sup></h3>
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- Houston, TX
- remote
- Temporary
-
- USD / Hourly
- <p><strong>Key Responsibilities</strong></p><ul><li>Design, implement, and own ETL/ELT pipelines—both batch and real‑time—using best-in-class tools and frameworks.</li><li>Architect and maintain scalable data platforms: data lakes, warehouses, and modern lakehouses.</li><li>Drive adoption of event-driven systems, ensuring schema evolution, idempotency, and end-to-end pipeline reliability.</li><li>Leverage AI-powered tooling and automation to optimize operations and reduce manual overhead.</li><li>Implement robust DataOps practices, including CI/CD, version control (Git), testing, and observability.</li><li>Collaborate with platform and AI teams to build AI‑ready, trustworthy data systems with lineage, governance, and cost-awareness.</li><li>Monitor and optimize performance and cost across cloud services</li></ul><p><br></p><p><br></p>
- 2025-12-29T18:34:00Z