Data Engineer

About the Role
We are seeking an experienced Data Engineer with at least 4+ years of professional experience to design, build, and optimize modern data infrastructure supporting enterprise analytics, artificial intelligence, machine learning, reporting, and digital products.
New Jersey is our primary hiring location, with hybrid and remote opportunities available depending on the position and business requirements. We are also particularly interested in qualified candidates located in Pennsylvania, California, Louisiana, North Carolina, Florida, Texas, and New York for remote opportunities.
The Data Engineer will work with Data Engineering, Data Science, Machine Learning, Analytics, Software Engineering, Cloud, Product, and business teams to develop scalable data pipelines and reliable enterprise data platforms.
This position requires hands-on experience with modern ETL/ELT, SQL, Python, cloud data platforms, data warehouses, data lakes/lakehouses, orchestration, and large-scale data processing.
You will help transform raw information from multiple enterprise sources into secure, reliable, high-quality datasets that can support analytics, machine learning, AI applications, and critical business decisions.
The ideal candidate combines strong software and data-engineering fundamentals with practical experience building production-grade data solutions.
Essential Duties and Responsibilities
Design, develop, test, and maintain scalable ETL/ELT data pipelines.
Build batch and real-time data-processing solutions for structured and unstructured data.
Develop and optimize enterprise data warehouses, data lakes, and lakehouse architectures.
Integrate data from databases, APIs, enterprise applications, cloud platforms, and event streams.
Develop reusable data models and transformation frameworks.
Optimize SQL queries, pipeline performance, storage, and compute utilization.
Implement automated data-quality validation, monitoring, lineage, and operational alerting.
Develop orchestration workflows using technologies such as Apache Airflow, Azure Data Factory, AWS Glue, or comparable platforms.
Support large-scale data processing using Apache Spark, Databricks, or similar technologies.
Partner with Data Scientists and ML Engineers to create reliable datasets and pipelines for machine-learning and AI applications.
Troubleshoot pipeline failures, data-quality issues, and production data incidents.
Support CI/CD, automated testing, version control, and modern DataOps engineering practices.
Apply appropriate data security, privacy, access-control, and governance standards.
Job Qualifications and Requirements
At least 4+ years of professional Data Engineering, Data Platform Engineering, or closely related experience required.
Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Systems, or a related technical discipline, or equivalent professional experience.
Strong proficiency in SQL and Python.
Hands-on experience building production-grade ETL/ELT pipelines.
Experience with at least one major cloud environment: AWS, Microsoft Azure, or Google Cloud Platform (GCP).
Experience with modern data platforms such as Databricks, Snowflake, Redshift, BigQuery, Azure Synapse, or comparable technologies.
Knowledge of Apache Spark, PySpark, or other distributed data-processing technologies.
Experience with workflow orchestration technologies such as Airflow, AWS Glue, Azure Data Factory, or similar tools.
Strong understanding of relational databases, dimensional modeling, data warehousing, and analytical data models.
Understanding of data lakes, lakehouse architecture, and cloud-native data architecture.
Familiarity with Kafka or other event-streaming technologies is advantageous.
Experience with Git, automated testing, CI/CD, and Infrastructure as Code is valuable.
Understanding of data governance, security, quality, lineage, and access-control principles.
Personal Capabilities and Qualifications
Strong analytical and technical problem-solving capabilities.
High standards for data accuracy, reliability, and engineering quality.
Ability to understand complex data relationships and system dependencies.
Strong ownership of production data pipelines and platforms.
Ability to balance scalability, performance, maintainability, and cloud cost.
Effective communication with both technical and business stakeholders.
Comfortable independently troubleshooting complex data issues.
Effective collaboration within distributed and hybrid engineering teams.
Curiosity about cloud technologies, artificial intelligence, machine learning, and evolving data architectures.
Strategic Support
Support modernization of enterprise data platforms and legacy data environments.
Contribute to cloud migration and data-platform transformation initiatives.
Help establish scalable data foundations for analytics, machine learning, Generative AI, and enterprise reporting.
Improve enterprise data availability, quality, and accessibility.
Contribute technical expertise to data-architecture and platform roadmaps.
Help establish reusable engineering standards and automated DataOps practices.
Support enterprise data-governance, security, and responsible AI initiatives.
Working Conditions
Primary hiring location: New Jersey — Hybrid / Remote.
Remote candidates may also be considered, with preference for qualified candidates located in PA, CA, LA, NC, FL, TX, and NY.
Full-time position within a large enterprise technology environment.
Hybrid employees may be expected to attend an assigned office periodically based on team and business requirements.
Remote employees will collaborate virtually with distributed technical and business teams.
Cross-time-zone collaboration may occasionally be required.
Occasional participation in production support or critical data-platform activities may be necessary.
Responsible handling of confidential, proprietary, customer, and enterprise data is required.
Job Function
Primary Function: Data Engineering
Specialization: Cloud Data Engineering & Enterprise Data Platforms
Employment Type: Full-Time
Workplace: Hybrid / Remote
Primary Market: New Jersey
Additional Preferred Markets: PA | CA | LA | NC | FL | TX | NY
Experience Required: 4+ Years
Relevant Skills & Keywords: Data Engineer, Data Engineering, Senior Data Engineer, Python, SQL, ETL, ELT, Data Pipelines, Data Warehousing, Data Modeling, Data Lake, Lakehouse, Databricks, Snowflake, Apache Spark, PySpark, Apache Airflow, Kafka, AWS, Azure, GCP, Azure Data Factory, AWS Glue, Redshift, BigQuery, Azure Synapse, dbt, DataOps, CI/CD, Cloud Data Engineering, Data Architecture, Data Quality, Machine Learning Data Pipelines, AI Data Infrastructure.
Compensation & Benefits
Compensation will be competitive and determined based on relevant experience, technical expertise, qualifications, geographic location, and overall 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
Hybrid and remote-work support
Professional certification and technical-development resources
Cloud, data, and AI learning opportunities
Wellness and employee-assistance programs
Additional benefits according to company policy
Why Join Us
Join a large enterprise environment where data engineering is foundational to analytics, AI, machine learning, and digital transformation.
You will have opportunities to work with modern technologies including Python, SQL, Databricks, Snowflake, Spark, cloud platforms, data lakes, lakehouse architecture, and automated data pipelines while solving complex data problems at enterprise scale.
The role offers meaningful technical ownership, exposure to modern cloud and AI initiatives, collaboration with experienced Data and Engineering professionals, and continued growth within a sophisticated enterprise technology organization.
For an experienced Data Engineer with 4+ years of professional experience, this opportunity provides the scale, technology, flexibility, and technical challenges to make a meaningful impact.

Work arrangement
Yes