> Markdown version of [/jobs/ext/3986428-data-bricks-engineer-with-aws-local-to-ma](https://www.wearedevelopers.com/jobs/ext/3986428-data-bricks-engineer-with-aws-local-to-ma). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Bricks Engineer with AWS - Local to MA - **Company:** Binary Computer Int'l Corp. - **Location:** Boston, MA, United States - **Experience:** Expert - **Contract:** Contract - **Skills:** Amazon Web Services, Cloud Database, System Configuration, Information Engineering, Extract Transform Load (ETL), Data Transformation, Database Queries, Distributed Computing Environment, Job Scheduling, Query Optimization, Data Processing, Data Ingestion, Apache Spark, Data Pipelines, Databricks - **Published:** October 28, 2026 - **Apply:** https://www.dice.com/job-detail/40770bda-bf30-4181-9ddf-6608c3c20c72 ## About the Role * 12 years of overall experience in Scala/Spark data engineering and data pipeline design. * Strong hands-on experience with Scala and Apache Spark programming. * Strong SQL skills, including complex queries, joins, aggregations, and query optimization. * Hands-on experience designing and developing data ingestion pipelines, ETL pipelines, and data processing jobs. * Experience with AWS services and cloud-based data engineering solutions. * Strong understanding of distributed data processing, data transformations, and pipeline architecture. * Experience troubleshooting and optimizing Spark applications and data workflows. * Strong analytical, problem-solving, and communication skills. * Ability to participate in technical coding assessments and explain implementation decisions. Preferred Skills * Hands-on experience with Databricks. * Experience configuring and managing Databricks workspaces and clusters. * Experience with Databricks job scheduling and workflow management. * Experience developing and maintaining production-grade ETL and ingestion pipelines in Databricks. * Familiarity with data quality, pipeline monitoring, and production support practices.