Leads the architecture, development, and operation of scalable enterprise data platforms, pipelines, ETL/ELT workflows, and transactional lakehouses. Uses AWS Glue, Apache Spark, Apache Hudi, and Amazon Athena to deliver production-grade data solutions. Provides data architecture leadership, code reviews, technical standards, query optimization, CI/CD integration, troubleshooting, and mentoring while collaborating with clients, product managers, and cross-functional teams.
ImagineX is a tech company that deploys AI-assisted teams to build and secure mission-critical enterprise solutions with our clients – spanning software, data, and AI. Structured like a software company, not a traditional consultancy, we blend deep technical expertise with authentic values, achieving world-class satisfaction (NPS 91). Our dedicated teams specialize in software, data, and AI across the U.S. and LATAM, bridging the gap between boutique agility and enterprise scale.
As a Senior Data Engineer, you will lead the design, implementation, and operationalization of modern enterprise data platforms. You will be responsible for architecting scalable data solutions, driving technical best practices, and mentoring other engineers while remaining hands-on with the code. This role is ideal for a seasoned engineer who thrives in complex data environments, possesses strong leadership skills, and is passionate about building production-grade systems using modern SQL, AWS analytics, and distributed computing stacks.
We are open to full-time or 1099 contractors. Although this position is 100% remote, you must be comfortable working East Coast hours.
Responsibilities
As a Senior Data Engineer, you will lead the design, implementation, and operationalization of modern enterprise data platforms. You will be responsible for architecting scalable data solutions, driving technical best practices, and mentoring other engineers while remaining hands-on with the code. This role is ideal for a seasoned engineer who thrives in complex data environments, possesses strong leadership skills, and is passionate about building production-grade systems using modern SQL, AWS analytics, and distributed computing stacks.
We are open to full-time or 1099 contractors. Although this position is 100% remote, you must be comfortable working East Coast hours.
Responsibilities
- Lead the development and maintenance of robust, scalable data pipelines and ETL/ELT workflows using AWS Glue and Apache Spark.
- Architect and build complex, transactional data lakehouses leveraging Apache Hudi for efficient ingestion and data management.
- Guide design discussions, conduct rigorous code reviews, and provide data architecture leadership for development squads.
- Collaborate with cross-functional team members, product managers, and client stakeholders to deliver high-quality data features and business insights from conception to deployment.
- Establish data engineering standards, drive clean code initiatives, and champion best practices across the data development lifecycle.
- Leverage AI-assisted tools to optimize development workflows, automate data testing, and improve overall pipeline reliability.
- Troubleshoot, debug, and optimize high-scale analytical queries within Amazon Athena and distributed data systems for peak performance and security.
- Oversee CI/CD pipeline integration, cloud data infrastructure deployments, and workflow automation efforts.
- Mentor junior and mid-level data engineers, fostering a culture of technical excellence and continuous growth.
- 7+ years of professional software or data engineering experience.
- Expert proficiency in SQL and advanced relational data management techniques.
- Strong hands-on experience building distributed data processing frameworks using Apache Spark.
- Proven experience architecting cloud data solutions, serverless execution with AWS Glue, and interactive analytics via Amazon Athena.
- Deep understanding of modern open-source table formats, specifically constructing transactional lakehouse layers with Apache Hudi.
- Deep understanding of data engineering fundamentals, dimensional modeling, testing strategies (data quality validation, unit, integration), and version control (Git).
- Strong communication skills, with the ability to articulate complex technical data concepts to both technical and non-technical stakeholders.
- Proven ability to lead technical teams, mentor engineers, and drive successful data project delivery.
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