Top Data Engineer Jobs in NYC, NY

Build and architect scalable cloud data platforms, pipelines, and applications using technologies such as Python, Spark, Databricks, and Snowflake. Lead large-scale data initiatives, make architectural decisions, establish engineering patterns, and ensure platform resilience and operational efficiency. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical concepts to stakeholders. Evaluate data platform choices and deliver solutions supporting machine learning, distributed systems, and evolving enterprise data needs.

Lead large-scale data engineering initiatives, designing scalable cloud data platforms, pipelines, and architectures. Use Python, Spark, SQL, Databricks, Snowflake, AWS, relational and NoSQL databases to deliver resilient data solutions. Mentor engineers, analysts, and data scientists; establish engineering standards; evaluate platform choices; and collaborate with product and software teams to support enterprise data transformation and AI capabilities.

Designs, builds, and operates scalable, resilient cloud data platforms and pipelines. Leads large-scale data initiatives, makes architectural decisions, evaluates technologies such as Snowflake and Databricks, and develops solutions using distributed data systems, relational and NoSQL databases, and cloud platforms. Collaborates with product and engineering teams, establishes reusable design patterns, communicates technical concepts, and mentors data engineers, analysts, and scientists.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Architect reusable engineering patterns, improve resilience and operational efficiency, enforce data security, and support real-time workloads. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical outcomes to stakeholders.

Design, build, deploy, and support scalable cloud data solutions, pipelines, platforms, and applications for enterprise risk management. The role uses Python, Spark, SQL, relational and NoSQL databases, Databricks, Snowflake, and cloud technologies. Responsibilities include data modeling, distributed and streaming workloads, security controls, architecture standards, observability, operational efficiency, stakeholder communication, mentoring, and collaboration with product, engineering, analytics, and machine learning teams.

Leads enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, AI, and vector-store use cases. Establishes data quality, governance, lineage, observability, access-control, and reliability standards. Defines AI-enabled data engineering patterns, mentors engineers, resolves complex architecture issues, and influences cross-functional roadmaps. Requires hands-on technical leadership and 15+ years of software, data engineering, or architecture experience.

Designs, builds, and operates scalable cloud data pipelines, platforms, and applications. Responsibilities include data modeling, distributed data processing, relational and NoSQL database development, architecture decisions, observability, streaming pipelines, and data warehousing. The role leads large-scale data initiatives, evaluates technologies such as Snowflake and Databricks, collaborates with product and engineering teams, communicates technical concepts, and mentors data engineers.

Build and operate scalable cloud data platforms and pipelines for Capital One’s Risk Tech organization. Responsibilities include designing distributed data solutions, enforcing engineering patterns, implementing security controls, collaborating across Agile teams, mentoring developers and analysts, and delivering AI-powered risk management products. The role requires expertise in Python, Spark, SQL, databases, cloud data warehousing, data modeling, and pipeline development.

Build, optimize, and maintain scalable data pipelines and reusable datasets for business intelligence, marketing, and data science. Develop a federated data validation framework, optimize queries and data models, and protect sensitive healthcare data through privacy and security best practices. The role uses Python, SQL, AWS, Airflow, PostgreSQL, Snowflake, and modern data-stack technologies. This position is based in New York City and requires working onsite three days per week.

Build and maintain scalable data pipelines supporting reporting, analytics, and AI/ML use cases. Write production Python and SQL code, integrate data from APIs and other sources, establish data governance, implement quality monitoring and alerting, support production incidents, and document technical solutions. Collaborate with data scientists, engineers, and business partners to deliver reliable data infrastructure and insights.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Designs and develops scalable data architectures, pipelines, data warehouses, and analytics solutions across on-premise, cloud, and hybrid environments. Performs data wrangling and discovery, collaborates on business intelligence initiatives, mentors data engineers, contributes to technical capability development, and supports project planning and estimation.

As a Quantitative Developer, you'll innovate and enhance quantitative methods, develop custom tools, maintain data pipelines, and collaborate with researchers for advancing investment strategies.

Build and operate production data pipelines, models, and data products supporting finance, accounting, treasury, compliance, and regulatory reporting. Partner cross-functionally to define requirements, model on-chain and internal data, and ensure data reliability through reconciliation, quality checks, monitoring, and alerting. Use SQL, Python, cloud data warehouse, Airflow, dbt, and AI tooling. Mentor engineers, review technical work, document systems, and communicate data concepts to technical and non-technical audiences.

Develop and maintain Java applications, RESTful APIs, backend services, and cloud-ready integrations using AWS. Write optimized SQL, automate tasks with Bash, manage code with Git, troubleshoot production issues, and participate in architecture discussions, code reviews, and continuous improvement. The role requires ownership of end-to-end technical solutions and collaboration across teams.

Design, build, test, and support scalable cloud data solutions and pipelines. Develop data platforms using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases, and cloud technologies. Establish reusable data engineering patterns, security controls, and operational standards. Collaborate with product managers, engineers, analysts, and data scientists; communicate technical outcomes; mentor data professionals; and deliver resilient, high-performance financial data products.

Leads the architecture, development, modernization, and optimization of enterprise Databricks Lakehouse platforms. Designs batch and real-time pipelines, implements Spark and PySpark solutions, and establishes governance, data quality, security, monitoring, and CI/CD practices. Provides technical leadership, mentors engineering teams, manages delivery risks, and translates business requirements into scalable cloud data solutions across Azure, AWS, or GCP. Supports analytics, AI/ML, and business intelligence initiatives while optimizing platform performance, cost, reliability, and compliance.

Leads enterprise-scale Databricks Lakehouse architecture, data platform modernization, and batch or real-time ETL/ELT development. Designs scalable Spark and PySpark solutions across cloud platforms, establishes governance, security, quality, lineage, and observability standards, and drives CI/CD and infrastructure automation. Provides technical leadership through architecture and code reviews, mentoring, stakeholder engagement, delivery planning, and executive communication. Supports analytics, AI/ML, and business intelligence ecosystems while optimizing platform performance, reliability, and cost.

Lead the technical direction, architecture, reliability, observability, and modernization of a cloud-based data engineering platform. Design scalable pipelines and reusable frameworks supporting analytics and AI, improve data quality, and establish testing, code review, CI/CD, and engineering standards. Drive adoption of AI-assisted development tools and build agentic workflows across planning, coding, testing, and deployment. Partner with machine learning, MLOps, Analytics Engineering, and business stakeholders in a highly autonomous senior individual contributor role.

Build and support Python data applications and infrastructure, migrate systems to Azure cloud-native architecture, develop automated ETL pipelines, and design scalable hybrid cloud and on-premises solutions. The role also implements statistical, machine learning, and deep learning models, improves data science infrastructure and processes, and documents technology architectures and deployments.

Build and support end-to-end data pipelines using Fivetran, Snowflake, and dbt. Develop data models, tests, documentation, incremental processes, and optimized SQL. Manage Snowflake security and data-loading patterns, implement data quality monitoring, troubleshoot production issues, and support CI/CD deployments. Collaborate with technical and business teams to onboard data sources and deliver enterprise data products. Python, orchestration, AWS, and AI-assisted development experience are beneficial.

Build and maintain production data pipelines, integrations, semantic models, AI agents, copilots, and automated workflows using business data. Partner with Sales, Marketing, RevOps, and Customer Success to deliver reliable analytics and automation solutions. Establish data quality, monitoring, evaluation, access controls, and deployment practices while enabling colleagues to adopt AI tools effectively.

Build and maintain recruiting data pipelines, datasets, integrations, dashboards, automations, and data quality controls. Partner with recruiting, data, security, privacy, engineering, and business teams to improve operational reporting and workflows. Responsibilities include integrating ATS and HR systems, monitoring pipeline stability, supporting writeback processes, designing secure access solutions, documenting sensitive workflows, and adapting data products as recruiting processes evolve.

Build and lead large-scale cloud data engineering initiatives, including cross-cloud migrations, lakehouse architecture, scalable data pipelines, data modeling, and real-time data solutions. Use Python, Spark, Databricks, Snowflake, SQL, and relational and NoSQL databases. Independently make architectural decisions, evaluate platform choices, mentor engineers, collaborate with product and software teams, and deliver reliable data platforms powering personalized travel experiences.

Designs, develops, implements, and supports data applications, warehouses, and ETL systems. The role uses AWS Glue, Athena, Airflow, Snowflake, databases, Python, PySpark, Unix scripting, and Ab Initio. Responsibilities include systems analysis, application development, testing, implementation, risk assessment, security measures, user support, process standards, project management, and mentoring junior analysts. Mortgage originations and servicing experience is required, with Big Data and AI exposure preferred.