Data Engineer Jobs in Jersey City, NJ

Lead design, build, and operate scalable data platform components and pipelines (batch and real-time) using Spark, Flink, Kafka, Airflow and Databricks. Implement data modeling, governance, cataloging, observability, and production-grade code; partner with stakeholders and lead technical evaluations and communities of practice.

Lead and contribute to an agile engineering team building scalable, secure big-data applications. Provide technical guidance, design and produce production-quality code, drive architecture and tooling decisions, champion AI-assisted engineering practices, and collaborate across global teams to ensure operational stability and delivery.

Lead design and delivery of reliability, observability, and SRE practices for large-scale data and AI/ML platforms. Define NFRs, SLI/SLOs, incident response, and implement reliable, secure, scalable infrastructure and automation while mentoring teams and driving safe, auditable AI-assisted operations.

The Databricks Data Engineer will design and implement ETL pipelines, manage data ingestion processes, and collaborate with business teams to create analytics solutions.

Lead architecture and standards for CoreWeave's enterprise data ecosystem: design lakehouse/streamhouse architectures, modeling and semantic layers, governance, data quality, lineage, observability, and reusable data products. Drive cross-domain solutions, resolve performance and scalability constraints, evaluate core data technologies, conduct architecture reviews, and mentor senior engineers.

Lead the design and optimization of batch and streaming data pipelines, ensuring high-quality data delivery for AI and analytics, while mentoring and promoting engineering best practices.

Design, build, validate, and productionize AI/ML capabilities for infrastructure and operations. Apply NLP, ML, forecasting, anomaly detection, and embeddings to enterprise data; develop pipelines, APIs, feature workflows, prompts, monitoring, and governance artifacts; advance MLOps, CI/CD, model serving, and hybrid-cloud deployments while collaborating with infrastructure, risk, data science, and product teams.

Design, build, and maintain scalable ETL/ELT pipelines and Snowflake schemas; integrate APIs and event streams; implement orchestration (Airflow/Prefect/Dagster), data quality, lineage, CI/CD, Docker, and automated testing to ensure reliable, production-grade data platforms.

Lead architecture and hands-on development of cloud-native AI web applications. Design end-to-end solutions integrating LLMs, vector search/RAG, real-time data, and scalable backend services. Implement model serving, RAG pipelines, AI agents, and ensure quality via testing and CI/CD while leading cross-functional engineering teams and translating business requirements into production-ready systems.

The role involves developing Java-based applications, collaborating with business users, managing testing strategies, and supporting compliance in a regulatory context.

The role involves leading the design and delivery of multi-agent systems for underwriting and claims automation, ensuring production readiness and business alignment.

Design, build, and maintain scalable data pipelines and infrastructure on AWS, collaborating with data scientists and AI teams to support analytics and ML initiatives.

Design, build, and maintain end-to-end batch and streaming data pipelines on GCP. Migrate legacy warehouses to BigQuery/Bigtable/ClickHouse, orchestrate workflows in Airflow, ensure data quality and observability, and partner with ML engineers on feature pipelines, drift monitoring, and retraining.

Lead ETL and data architecture for AML, trade/sales surveillance, and compliance data repositories. Design and develop complex Informatica PowerCenter 9.1 mappings, enforce ETL architecture standards, tune performance, automate and maintain ETL utilities, collaborate with BAs and DBAs, provide production support, and mentor junior ETL developers.

This role involves designing and implementing large-scale big data platforms using Java and various big data frameworks, along with integrating systems and managing databases.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Join Speechify as a Software Engineer responsible for data collection to support model training, enhancing the ingestion pipeline and improving data quality at scale.

Design, develop, maintain and test scalable big data solutions. Build preprocessing and reporting pipelines, collect/parse/analyze/visualize large datasets, test and deploy machine-learning models on big data, and work in agile teams to deliver analytics solutions.

Design, build, maintain, and test big data solutions and preprocessing/reporting pipelines. Collect, parse, manage, analyze, and visualize large datasets. Test and deploy machine learning models for scoring and prediction. Collaborate in agile teams to deliver scalable, reusable code and production-ready analytics on cloud platforms.

Design, develop, test and maintain scalable big-data solutions and workflows. Collect, parse, analyze and visualize large datasets; evaluate, test and deploy machine-learning models for scoring and prediction. Work in an agile team, automate tasks using scripting, and leverage Hadoop/Spark and AWS for production pipelines.

Lead design and implementation of scalable batch and streaming data pipelines and platforms. Define data architecture standards, drive engineering best practices, mentor peers, and collaborate with product, analytics, and data science to deliver reliable, high-performance data solutions.

Develop and maintain real-time market data feeds, focusing on performance and latency improvements in a collaborative environment.

Lead design and implement scalable PySpark data pipelines in a Hadoop ecosystem. Build batch and real-time workflows, optimize distributed processing, write complex SQL, automate scheduling with shell/Autosys, design data models, resolve technical risks, and coach mid-level engineers to meet business goals while ensuring compliance and data integrity.

Lead a team to define architecture and roadmap for enterprise data lake and analytics platforms. Design and deliver large-scale batch and real-time pipelines, feature stores, metadata/lineage, and automated data quality frameworks to support production ML, advanced analytics and GenAI. Collaborate with data scientists, product and enterprise architects to evaluate emerging data technologies.

The Big Data Engineer will develop and maintain big data solutions, collaborate in an agile team, and implement machine learning models to drive analytics projects.