Top Data Engineer Jobs in NYC, NY

The Lead Data Engineer designs and develops technical solutions, collaborates with Agile teams, and utilizes big data technologies, cloud computing, and full-stack development to drive digital transformation at Capital One.

Lead and mentor a data engineering team to design, develop, test, and support cloud-based big data and streaming solutions. Build full-stack data pipelines using Java/Scala/Python, RDBMS/NoSQL, Redshift/Snowflake, and cloud platforms. Collaborate with product managers, perform unit tests and code reviews, and promote engineering best practices and tech community engagement.

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.

Build and maintain ingestion pipelines for heterogeneous healthcare data (TPA feeds, claims, eligibility, enrollment). Design dbt models and orchestration (Dagster/Airflow), implement data quality monitoring and observability, and partner with data science to produce and maintain feature pipelines and feedback loops.

Scope, implement, validate, and monitor merchant data integrations to ensure accurate input to data science models. Maintain data configuration, create JSON/CSV templates, write Python/SQL/Spark/Databricks/R scripts for testing and transformation, upload historical data, triage alerts, and coordinate with internal and client teams to resolve data issues and improve pipelines.

Support the design, build, and maintenance of scalable data pipelines and data management systems using cloud platforms. Assist in ETL, data transformation, reporting, and dashboard creation (Power BI/Tableau). Troubleshoot pipelines, help integrate new technologies (including AI/ML tools), ensure data security/compliance, and communicate with finance/accounting stakeholders across multiple client engagements.

Lead data engineer responsible for designing and building scalable databases, ETL pipelines, and data messaging platforms. Develop and optimize SQL/stored procedures, implement Spark/Snowflake/Databricks solutions, automate processes with Python and shell scripts, and collaborate with global business stakeholders using Agile practices to deliver accurate, accessible data for operational stores, archives, and analytics.

The Senior Lead Data Engineer designs and develops technical solutions, leads a team of developers, and utilizes big data and cloud technologies to deliver robust solutions for Capital One.

The Data Engineer will design and implement ETL frameworks, perform data analysis, troubleshoot data issues, and optimize data models to meet business needs.

Lead design and implementation of scalable cloud data lakes, warehouses, and marts. Build ELT/ETL pipelines, apply Data Vault 2.0 and dimensional modeling, implement data quality, lineage, observability, and governance. Optimize Snowflake/Databricks/Synapse platforms, champion CI/CD and DataOps, mentor engineers, and enable AI/ML-ready data products through collaboration with architecture and product teams.

Build and operate large-scale data pipelines (batch and streaming) for multi-terabyte audio/video datasets. Deploy and scale containerized workloads on Kubernetes/AWS, maintain Spark/Ray jobs, run workflow orchestration (Airflow), tune enterprise databases, and collaborate with ML teams on training and MLOps workflows.

Design, build, and operate production data pipelines and architectures across Snowflake and Databricks. Scale demo data across cloud warehouses, partner with infrastructure for high availability, implement data governance, and collaborate cross-functionally to support analytics and GTM use cases.

Design, build, and maintain scalable batch and streaming data pipelines and data products. Write and document code (Python, SQL, Spark), implement data quality checks, monitor pipeline reliability, and troubleshoot issues. Collaborate with analysts, data scientists, and product managers to translate business needs into engineering tasks and follow agile practices. Contribute to observability, testing, documentation, and team best practices.

Lead Data Engineer designs, builds, tests, and supports cloud-based data platforms for marketing and messaging. Collaborates across Agile teams to deliver scalable, real-time data and ML-enabled solutions using distributed systems, data warehousing, ETL, and streaming technologies. Mentors engineers and participates in code reviews and performance tuning.

Lead design and delivery of scalable, secure cloud data pipelines and products. Translate requirements into architecture, apply advanced data modeling (Data Vault 2.0/dimensional), embed data quality, observability, security, and DataOps/CI-CD practices, optimize performance and cost, mentor engineers, and enable AI/ML-ready datasets within an agile DevSecOps model.

Design, build, and operate scalable cloud data pipelines and platforms (Azure preferred). Implement DevSecOps, CI/CD, automated testing, monitoring, performance tuning, and production readiness. Mentor engineers, drive operational excellence, and translate requirements into robust technical designs. Apply healthcare domain knowledge and collaborate in agile teams to deliver secure, reliable data products.

Design, build, and automate scalable data platforms (lakehouses/warehouses). Implement and optimize ETL/ELT pipelines using Databricks and dbt, apply Medallion Architecture, manage IaC/CI-CD, ensure data security and governance, and integrate pipelines with BI tools while engaging with stakeholders.

As a Senior Data Engineer, you'll design and build data pipelines and architectures, ensuring data quality and governance while mentoring peers.

Build and maintain data pipelines and transformation layer using dbt and SQL; monitor replication and orchestration pipelines, add data quality checks, and develop datasets and dashboards for internal and client analytics. Collaborate with senior engineers, participate in code reviews, and gradually take on more ownership.

Senior Addepar data engineer/analyst responsible for supporting and scaling Addepar integrations, building and maintaining data pipelines (Addepar↔Snowflake/Canoe/other platforms), diagnosing and resolving data quality issues, developing SQL-based reconciliations and automation, partnering with business and technology stakeholders, and supporting production operations, governance, testing, and documentation for wealth and asset management data.

Build and operate GCP-native data pipelines and lakehouse infrastructure using Airflow, dbt, and BigQuery. Design layered data models, manage ingestion (batch and streaming), implement observability and governance, and maintain CI/CD workflows to deliver reliable analytics for MLB stakeholders.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.