Data Engineer Jobs in Jersey City, NJ

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

Own and evolve the data lake and analytics stack powering fleet observability. Build and maintain scalable ETL/ELT pipelines, operate data lake infrastructure (Iceberg, Trino, Airflow, Spark, Superset), produce analytics and executive reporting, create dashboards and runbooks, and implement data governance and observability.

Lead design and delivery of complex data solutions including data integration, cleaning, transformation, lineage, and migration. Build and document data pipelines, manage ADS and regulatory data lineage, lead testing and cutover for database migrations, create automation and reconciliation scripts, troubleshoot with architects and security teams, and mentor other data engineers.

Design, build, and operate petabyte-scale data pipelines for AI training and evaluation. Responsibilities include multimodal ingestion, data cleaning and QA, dataset versioning and lineage, high-throughput loading for accelerator training, labeling and human-in-the-loop workflows, storage architecture, privacy/redaction enforcement, observability of data quality, and collaboration with ML researchers to optimize model training efficiency and cost.

Design, build, and operate petabyte-scale data pipelines and storage to support AI training and evaluation across multimodal datasets. Implement data ingestion, cleaning, versioning, lineage, high-throughput loading for accelerator-based training, labeling workflows, privacy controls, and observability while collaborating with ML researchers to optimize model training and data quality.

Lead design and delivery of enterprise-grade CRM and analytics data integration solutions. Build and operate batch and near-real-time ETL/ELT pipelines, Python automation, and SQL/NoSQL data models. Ensure data quality, security, observability, and operational ownership while partnering with architecture, application, and business teams.

Lead development of AI and ML solutions for healthcare systems, overseeing teams, driving use case development, and ensuring operational excellence while managing global delivery 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 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.

Manage delivery of complex data pipelines in a hands-on role, leading a team of data engineers to develop scalable data solutions and improve analytics capabilities.

As a Sr Data Engineer, you'll conduct feasibility analyses, develop applications, monitor processes, consult with users, and ensure compliance while serving as a mentor to junior analysts.

The Senior Market Data Platform Engineer will design and implement a low-latency market data distribution platform, focusing on performance optimization, architectural decisions, and collaboration with global teams, particularly in Equities trading.

As a Data Engineer, you will design and implement scalable data architectures, oversee integration and governance while collaborating on Master Data Management frameworks, to optimize data performance and drive innovation across the organization.

The Data Engineer designs and builds data pipelines for analytics, ensuring data quality, governance, and integration across systems, while collaborating with teams for effective delivery.

Design, build, and operate large-scale Hadoop-based data pipelines and streaming systems. Implement ETL/ELT with Spark, Hive, Sqoop; optimize jobs, ensure data quality and governance, automate orchestration, monitor pipelines, collaborate with data scientists, and mentor junior engineers.

The Data Engineer will develop and optimize data pipelines for LLMs, ensuring high-quality healthcare datasets while collaborating with data scientists on model requirements.

Design, build, and operate large-scale Hadoop-based data pipelines and streaming systems. Develop ETL/ELT workflows with Spark, Hive, and Sqoop; implement Kafka/Spark Streaming or Flink pipelines; optimize job performance; maintain storage on HDFS/Hive/HBase and lakehouse formats; enforce data governance, monitoring, and orchestration (Airflow/Oozie); collaborate with data scientists; document architectures and mentor junior engineers.

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

Analyze transaction data to detect fraud, develop controls and rule-engine logic, automate data workflows, build dashboards and reports, partner with data science on fraud models, monitor model performance, and advise stakeholders on fraud strategy and risk.

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

Design and build scalable data lakes, warehouses, and lakehouse architectures. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest third-party APIs, optimize columnar formats and query performance, and consult with stakeholders to translate business goals into data architecture roadmaps using Spark, Snowflake, and cloud infrastructure.