Top Senior Data Engineer Jobs in NYC, NY

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.

Leads enterprise data engineering, analytics, governance, and AI initiatives for Corporate & Investment Banking Finance. Designs scalable data architectures, pipelines, curated datasets, semantic models, and cloud reporting platforms. Drives data quality, lineage, metadata management, reporting modernization, workflow automation, generative AI adoption, and responsible AI practices. Partners with senior Finance, Technology, and Data Management stakeholders to deliver enterprise data products and transformation programs.

Designs, develops, validates, and documents analytics data solutions for diverse client environments. Builds Snowflake, SQL, dbt, and AWS ingestion and transformation pipelines; monitors data quality and performance; troubleshoots complex technology issues; and supports reporting and business insights. Manages project scope, budgets, documentation, governance, client communication, implementation planning, and quality assurance while collaborating across teams and identifying opportunities for additional solutions.

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.

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 and maintains scalable data pipelines and infrastructure, establishes data governance and quality standards, and drives technical decisions across Headway’s data platform. Partners with Product, Engineering, Analytics, Data Science, Infrastructure, Finance, and Insurance Systems to improve architecture, performance, reliability, security, and AI-readiness. Ensures data is complete, traceable, trustworthy, and suitable for powering internal tools and AI-driven insights.

Build and architect scalable cloud data solutions, pipelines, platforms, and data products. Lead large-scale data initiatives, make architecture and platform decisions, ensure performance and operational efficiency, and apply modern technologies such as Python, Spark, Databricks, and Snowflake. Collaborate with Agile teams, product managers, software engineers, analysts, and data scientists while mentoring engineers and communicating technical concepts to stakeholders.

Design, build, and support scalable cloud data platforms, pipelines, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, data security, observability, orchestration, and real-time pipelines. It also requires influencing cross-functional teams, mentoring data professionals, establishing engineering standards, and communicating technical outcomes to stakeholders.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Designs, builds, and maintains the ASPCA’s enterprise data warehouse ecosystem. The role develops dbt Core models across medallion architecture, integrates MDM workflows, builds and operates Microsoft Fabric and Azure Data Factory pipelines, and supports Snowflake environments. Responsibilities include dimensional modeling, orchestration, monitoring, testing, troubleshooting, performance optimization, documentation, CI/CD, data governance, and quality assurance. The engineer collaborates with BI, data management, strategy, and research teams to deliver trusted, analytics-ready datasets.

Designs and optimizes scalable data pipelines, services, and models using Python and SQL. Ensures data quality, reliability, and performance across large-scale storage and processing systems. Collaborates with analysts, data scientists, engineers, and cross-functional teams to deliver actionable data solutions. Integrates LLM and agent-based capabilities into data platforms and supports clinical, business intelligence, and operational needs.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, ensure data quality and reliability, and mentor engineers and analysts. Build, evaluate, and monitor production LLM and agent systems, automate marketing workflows with AI, and deliver high-quality Python and SQL solutions supporting Marketing, Sales, and R&D.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Own and build January’s foundational data systems, including end-to-end pipelines, data quality standards, legacy migrations, recovery processes, and documentation. Partner with engineers, analysts, operations, and product teams to establish trusted, scalable data infrastructure. Prioritize high-impact improvements, manage stakeholder needs, define ownership boundaries, and use AI to create increasingly self-healing systems.

Design and implement data integration solutions, build data pipelines, manage cloud resources, and develop real-time data flows using AWS technologies and Snowflake.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Design and build scalable data infrastructure, including lakehouse architecture, ClickHouse deployments, ETL/ELT pipelines, and data models connecting Postgres with analytics systems. Establish data quality, observability, performance, cost, and reliability standards. Collaborate with product and engineering teams, define data contracts and specifications, support real-time analytics, and use AI-assisted coding and LLMs to accelerate pipeline development and transformation.

Build and maintain dbt data models and pipelines for billing, payments, usage, and revenue data. Ensure data accuracy, reconciliation, integrity, and timely delivery while defining metrics and monitoring systems. Develop dashboards and self-service data products, support finance data operations, and collaborate with Billing Engineering, Finance, Accounting, and Product teams. Influence billing data architecture and engineering roadmaps.

Designs, builds, and maintains enterprise data pipelines, models, integrations, and analytical data stores. Performs ELT, data cleansing, validation, quality monitoring, performance tuning, and database development using SQL Server and related technologies. Collaborates with analysts, product managers, developers, and business stakeholders, supports CI/CD testing, resolves data and database issues, and leads software delivery within an Agile environment. Participates in on-call support and maintains regulatory compliance.

Lead design and implementation of data infrastructure, pipelines, and warehouses using Azure Data Factory, Databricks, and Snowflake. Manage data modeling, quality, security, and analytics frameworks. Lead client accounts, mentor teams, and drive innovation to deliver data-driven business insights.

Lead design and implementation of scalable data infrastructure, pipelines, and transformations using cloud platforms. Build and optimize data warehouses (Snowflake, Databricks), orchestrate workflows (Airflow, Hadoop), ensure data quality and security, and coach teams to deliver analytics solutions that drive tax practice insights and client outcomes.

Design and build data infrastructure, pipelines, and transformations using Azure Data Factory, Azure Databricks, and Snowflake. Develop data architecture and modeling, maintain data lakes/warehouses, ensure data quality and security, optimize performance, and collaborate with clients to deliver tailored data solutions and governance.

Staff Data Engineer responsible for setting technical direction, architecting scalable data platforms, leading cross-functional initiatives, improving reliability and performance, and enabling AI-assisted analytics and development. The role includes hands-on development with Python, SQL, cloud data warehouses, orchestration, infrastructure as code, Kubernetes, Kafka, and streaming pipelines, along with mentoring, technical advocacy, documentation, code reviews, and on-call troubleshooting.