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# Data Engineer

- **Company:** Enterprise Engineering, Inc.
- **Location:** St. Louis, MO, United States
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Java (Programming Language), Airflow, Amazon Web Services, Amazon S3, Cloud Computing, Cloud Engineering, Code Review, Data as a Services, Information Engineering, Extract Transform Load (ETL), Dataspaces, Data Warehousing, Distributed Systems, Amazon DynamoDB, Apache Hadoop, Meta-Data Management, Microsoft SQL Server, NoSQL, Oracle (Applications), Cloud Services, Apache Spark, Electronic Medical Records, Data Lakes, Data Lineage, Cassandra, AWS Glue, Apache Nifi, Data Lakehouse, Cloud Migration, Stream Processing, Data Pipelines
- **Published:** October 14, 2026
- **Apply:** https://www.careerjet.com/job/us44b6f6e58ea678299847259616124d20/eaa

## About the Role

* 8-10 yrs. experience years of experience delivering enterprise-scale Data Warehouse, Data Lake, or Data Lakehouse solutions.
 * Proven experience implementing multiple end-to-end data engineering projects within large-scale distributed computing environments.
 * Hands-on experience migrating ETL and analytics workloads from on-premises platforms to cloud-native environments.

Technical Expertise

 * Strong development experience using: Apache Spark, Scala or Java, Hadoop ecosystem technologies, Object Storage platforms
 * Experience building orchestration and workflow solutions using: Apache Airflow/ Apache NiFi and Similar enterprise scheduling frameworks
 * Strong SQL expertise and experience with Relational and NoSQL database technologies: Oracle/ SQL Server, Cassandra, Dynamo DB etc.
 * Working knowledge of cloud platforms, preferably AWS: Amazon S3, EMR, AWS Glue, Cloud-native data services

Professional Skills

 * Strong analytical and problem-solving capabilities.
 * Experience operating within Agile delivery environments.
 * Excellent written and verbal communication skills.
 * Proven ability to collaborate within geographically distributed and matrix-based teams.
 * Self-starter with strong ownership, accountability, and execution focus.
 * Ability to learn emerging technologies quickly and apply them effectively to business challenges.

## Description

Data Engineering & Development

 * Design, develop, test, and deploy high-quality, secure, scalable, and resilient data pipelines using Apache Spark, Java/Scala across Hadoop and cloud-native object storage platforms.
 * Build and maintain batch and near real-time data processing frameworks capable of supporting petabyte-scale workloads.
 * Develop reusable engineering components and frameworks that accelerate data product delivery while maintaining enterprise standards.

Architecture & Platform Engineering

 * Design and implement a "build once, run anywhere " architecture supporting seamless deployment across on-premises and public cloud environments without code changes.
 * Implement data lineage, metadata management, data cataloguing, data quality controls, and observability capabilities across the data ecosystem.
 * Collaborate with architects and platform teams to establish scalable design patterns and engineering best practices.

Cloud Modernization

 * Contribute to migration initiatives moving legacy ETL and data warehouse workloads from on-premises environments to cloud-native architectures.
 * Leverage cloud services such as Amazon S3, EMR, Glue, and related data services to improve scalability, reliability, and operational efficiency.
 * Drive adoption of modern Lakehouse and distributed compute architectures.

Delivery & Technical Leadership

 * Lead end-to-end development activities including requirement analysis, solution design, coding, testing, deployment, and production support.
 * Mentor and guide junior engineers through code reviews, technical coaching, and engineering best practices.
 * Partner with product owners, analysts, architects, and business stakeholders to deliver high-quality solutions within committed timelines.

Operational Excellence

 * Troubleshoot complex production incidents and perform root cause analysis to identify and implement long-term remediation strategies.
 * Ensure compliance with Client's engineering, security, quality assurance, and operational governance standards.
 * Continuously identify opportunities to improve performance, automation, monitoring, and process efficiency.

Innovation

 * Evaluate emerging data technologies and conduct proof-of-concept (POC) initiatives to determine their applicability within Client's data ecosystem.
 * Contribute to engineering innovation and continuous improvement initiatives across the organisation.