Data Engineer
Essential Duties and Responsibilities Design, build, and maintain scalable data pipelines supporting analytics, reporting, machine learning, and enterprise applications. Develop reliable batch and real-time data processing workflows across structured and unstructured data sources. Build and optimize cloud-based data platforms, data warehouses, data lakes, and lakehouse architectures. Develop reusable data models and transformation frameworks that improve consistency across business domains. Integrate data from enterprise applications, APIs, databases, event streams, and third-party platforms. Improve pipeline performance, scalability, reliability, and cost efficiency. Implement automated data-quality checks, monitoring, lineage, and operational alerting. Develop orchestration workflows and support CI/CD practices for data engineering. Partner with Data Science, Analytics, AI/ML, Software Engineering, Product, and business teams to translate data requirements into production solutions. Diagnose complex data and pipeline failures and implement permanent corrective solutions. Support appropriate security, privacy, access-control, and data-governance standards. Contribute to modernization of legacy data environments and migration toward cloud-native architecture. Job Qualifications and Requirements Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Systems, or a related technical discipline, or equivalent practical experience. 3+ years of professional data engineering, software engineering, or data-platform experience preferred. Strong proficiency in SQL and at least one programming language such as Python, Java, Scala, or Go . Experience developing ETL/ELT pipelines and complex data transformations. Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform . Working knowledge of modern data platforms such as Snowflake, Databricks, BigQuery, Redshift, Synapse , or comparable technologies. Familiarity with technologies such as Apache Spark, Kafka, Airflow, dbt , or equivalent data-processing and orchestration frameworks. Understanding of relational, dimensional, and modern analytical data-modeling principles. Knowledge of data warehousing, data lakes, and lakehouse architecture. Familiarity with Git, automated testing, CI/CD, and Infrastructure as Code is advantageous. Understanding of data security, governance, quality, and access-management principles. Personal Capabilities and Qualifications Strong analytical and technical problem-solving ability. High standards for data accuracy, reliability, and engineering quality. Ability to understand complex data relationships and system dependencies. Strong ownership of production pipelines and data products. Ability to balance scalability, performance, maintainability, and cost. Clear written and verbal communication skills. Effective collaboration within distributed technical teams. Comfortable independently investigating complex technical problems. Curiosity about cloud technologies, AI infrastructure, and evolving data-engineering practices. Strategic Support Support enterprise cloud and data-platform modernization initiatives. Help establish scalable data foundations for analytics, machine learning, and generative AI. Contribute technical expertise to enterprise data architecture and platform roadmaps. Improve data availability and quality across critical business functions. Support migration from legacy ETL environments toward modern cloud-native data platforms. Help establish reusable engineering standards that accelerate delivery across data teams. Contribute to data-governance, security, and responsible AI initiatives. Working Conditions Remote position within the United States. Full-time position within a large enterprise technology environment. Regular collaboration with Data, Analytics, AI/ML, Engineering, Product, Security, and business teams. Cross-time-zone collaboration may be required. Occasional participation in production support or critical data-platform activities may be necessary. Requires appropriate handling of confidential, proprietary, customer, and enterprise data. Job Function Primary Function: Data Engineering Specialization: Cloud Data Platforms & Data Infrastructure Industry: Enterprise Technology, Data Platforms, Cloud & AI Employment Type: Full-Time | Remote USA Core areas include data pipelines, ETL/ELT, data modeling, cloud data platforms, data warehouses, lakehouse architecture, streaming data, orchestration, data quality, and AI/ML data infrastructure. Compensation & Benefits Compensation will be competitive and determined based on technical experience, qualifications, geographic considerations, and position scope. The total rewards package may include: Annual performance incentives Equity or long-term incentives where applicable Medical, dental, and vision coverage Retirement savings with employer contributions Paid time off and company holidays Paid parental and family leave Life and disability coverage Remote-work support Professional certifications and technical-development resources Wellness and employee-assistance programs Additional benefits according to company policy
Why Join Us
Join a large-scale organization where data is a foundational part of how products are built, decisions are made, and emerging AI capabilities are developed. As a Data Engineer, you will work on modern data infrastructure supporting high-value analytics, enterprise applications, machine learning, and AI use cases. You will have opportunities to solve complex data problems, work with modern cloud technologies, and influence how information moves across a sophisticated technology ecosystem. This role provides strong technical growth and meaningful ownership for an engineer who wants to build reliable, scalable, secure, and AI-ready data platforms at enterprise scale.