Data Engineer

About the Role We are seeking an experienced Data Engineer with a minimum of 4 years of professional experience to design, develop, and maintain scalable data infrastructure supporting enterprise analytics, business intelligence, machine learning, and AI-driven applications. This position offers an opportunity to work within a large enterprise technology environment, collaborating with Data Engineering, Data Science, Cloud Infrastructure, Software Engineering, Business Intelligence, and Product teams to deliver reliable, secure, and high-performance data solutions. The Data Engineer will develop and optimize modern ETL/ELT pipelines, cloud data warehouses, data lakes, lakehouse architectures, and distributed data-processing systems . The ideal candidate brings strong technical expertise in Python, SQL, cloud data platforms, data modeling, orchestration, and automated data pipelines , combined with an understanding of data quality, performance optimization, and production reliability. Essential Duties and Responsibilities Design, build, test, deploy, and maintain scalable ETL/ELT data pipelines for enterprise applications and analytics. Develop batch and real-time data-processing solutions using modern data engineering technologies. Build and optimize cloud data warehouses, data lakes, and lakehouse platforms . Integrate structured and unstructured data from databases, APIs, enterprise applications, and cloud services. Write and optimize complex SQL queries, data transformations, and data-processing workflows . Develop reusable data models supporting reporting, analytics, and machine learning. Implement data orchestration using Apache Airflow, AWS Glue, Azure Data Factory , or comparable platforms. Support large-scale data processing with Apache Spark, PySpark, Databricks , or similar frameworks. Establish automated data-quality validation, monitoring, and pipeline alerting. Troubleshoot data pipeline failures, performance issues, and data-integrity problems. Collaborate with Data Scientists and Analytics teams to deliver reliable, production-ready datasets. Support CI/CD, version control, automated testing, and modern DataOps practices. Apply data security, access-control, privacy, and governance standards. Job Qualifications and Requirements Minimum 4 years of professional experience in Data Engineering, Data Platform Engineering, or a closely related technical role required. Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Systems, or a related discipline, or equivalent practical experience. Strong hands-on proficiency in Python and SQL . Professional experience developing and maintaining production-grade ETL/ELT pipelines . Experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform (GCP) . Familiarity with Snowflake, Databricks, Amazon Redshift, Google BigQuery, Azure Synapse , or comparable enterprise data platforms. Experience with Apache Spark, PySpark , or distributed data-processing frameworks. Understanding of relational databases, dimensional modeling, data warehousing, and analytical data structures. Experience with workflow orchestration technologies such as Apache Airflow, AWS Glue, Azure Data Factory , or similar tools. Knowledge of data lakes, lakehouse architecture, and cloud-native data infrastructure . Familiarity with dbt, Kafka, streaming data, or event-driven architectures is advantageous. Experience using Git, automated testing, CI/CD pipelines, and Infrastructure as Code is valuable. Understanding of data governance, data security, data quality, and access-management principles. Strong technical troubleshooting, documentation, and communication skills. Personal Capabilities and Qualifications Strong analytical thinking and technical problem-solving skills. High standards for data accuracy, reliability, and engineering quality. Ability to understand complex data relationships and system dependencies. Strong ownership of data pipelines and production infrastructure. Ability to balance scalability, performance, maintainability, and cloud costs. Effective communication with technical and nontechnical stakeholders. Comfortable independently investigating complex technical issues. Strong collaboration within distributed engineering teams. Commitment to continuous learning in cloud data platforms, automation, and emerging technologies. Strategic Support Contribute to enterprise data-platform modernization and cloud migration initiatives. Support development of scalable data foundations for business intelligence, advanced analytics, machine learning, and artificial intelligence . Help improve enterprise data availability, consistency, and accessibility. Contribute technical expertise to data architecture and platform roadmaps. Support improvements in data governance, security, and operational reliability. Identify opportunities to improve data engineering productivity through automation. Help establish reusable data integration and transformation standards. Working Conditions Remote position within the United States. Full-time position within a large enterprise technology environment. Regular virtual collaboration with Data Engineering, Data Science, Cloud Infrastructure, Analytics, Product, and Software Engineering teams. Collaboration across multiple U.S. time zones may be required. Occasional participation in production support, major data-platform releases, or critical troubleshooting activities may be necessary. Responsible handling of confidential, proprietary, customer, and enterprise data is required. Job Function Primary Function: Data Engineering Specialization: Cloud Data Platforms, ETL/ELT & Data Infrastructure Workplace: Remote — United States Employment Type: Full-Time Experience Required: Minimum 4 Years Relevant Skills & Keywords: Data Engineer, Data Engineering, Cloud Data Engineering, Python, SQL, ETL, ELT, Data Pipelines, Data Integration, Data Warehousing, Data Modeling, Data Lake, Lakehouse Architecture, Snowflake, Databricks, Apache Spark, PySpark, Apache Airflow, dbt, AWS, Microsoft Azure, Google Cloud Platform, GCP, AWS Glue, Azure Data Factory, Amazon Redshift, Google BigQuery, Azure Synapse, Kafka, DataOps, CI/CD, Git, Data Quality, Data Governance, Data Architecture, Machine Learning Data Pipelines, AI Data Infrastructure. Compensation & Benefits Compensation will be competitive and determined based on technical experience, qualifications, geographic location, and overall position responsibilities. The total rewards package may include: Competitive annual base salary Performance-based incentives where applicable Medical, dental, and vision insurance Retirement savings with employer contributions Paid time off and company holidays Paid parental and family leave Life and disability insurance Remote-work support and equipment assistance Professional certification and technical training Cloud, data engineering, and AI learning opportunities Wellness and employee-assistance programs Additional benefits according to company policy Why Join Us Join a large enterprise technology environment where data engineering supports modern analytics, cloud transformation, machine learning, and AI innovation . You will have opportunities to work with sophisticated data platforms, complex datasets, and modern engineering technologies while collaborating with experienced professionals across Data, Cloud, AI, and Software Engineering. This role provides meaningful technical ownership, opportunities to improve enterprise data infrastructure, and continued growth in Python, SQL, ETL/ELT, Snowflake, Databricks, Spark, and cloud-native data architecture . For a Data Engineer with 4+ years of professional experience , this opportunity offers remote flexibility, technical challenges, and the ability to contribute to reliable, scalable data solutions with measurable business impact. Equal Employment Opportunity We are committed to providing equal employment opportunities to qualified applicants without discrimination based on legally protected characteristics. Employment decisions are based on job-related skills, experience, qualifications, and applicable business requirements.