Top Data Engineer Jobs in NYC, NY

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Lead data engineering efforts, manage data governance, ensure compliance, and oversee data platform economics while designing scalable systems.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Build and operate production data pipelines integrating ERP/ATS/project management/telemetry into a queryable layer. Own the data model for a live knowledge graph. Ship SLA-backed datasets/services for tools and ML. Convert unstructured vendor/field data (PDFs, spreadsheets) into trusted structured inputs. Ensure data quality via tests, monitoring, and lineage, and use modern data stacks and LLM tools responsibly.

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

As a Data Engineer, you will build and own the data infrastructure, design pipelines, ensure data quality, and implement DataOps practices while collaborating closely with product and engineering teams.

This role involves developing data pipelines, ensuring data performance, automating processes, and collaborating within Agile teams to meet complex data requirements.

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 Consultant, Data Engineer will design modern data platforms, build workflows, and translate business needs into technical solutions across various industries.

As an Analytics Engineer, you will create scalable solutions for data reporting, build data pipelines, and establish high data integrity standards while collaborating with multiple teams.

The Data Engineer will build and scale Baseten's internal data platform, designing data models and pipelines while enabling self-service analytics and ensuring data reliability.

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.

The Principal Data Engineer will lead complex data projects, design robust data pipelines, and drive architectural strategy for a scalable data ecosystem, while mentoring other engineers and ensuring the quality and usability of data solutions.

The candidate will improve the data ETL pipeline, build analytical tools, manage operations, and expand into new markets and asset classes.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Lead development of Marriott's cloud data platform, build and own large-scale ETL pipelines on AWS and Snowflake, mentor engineers, manage service provider deliverables, ensure governance, data quality, deployment and production support, and research emerging AI/ML capabilities.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

We are seeking a Data Engineer to design, build and optimize scalable data pipelines. Strong experience in Databricks, PySpark, and data warehouse concepts is required.

Design and build foundational dimensional data models, enterprise metrics, and curated data products on a modern lakehouse. Partner with business domains to translate processes into scalable, high-quality datasets, optimize analytical workloads, and operate distributed processing systems to support analytics, BI, and AI.

Design, build, and optimize enterprise-scale data models, pipelines, and cloud-native architectures to support analytics, AI/ML, and reporting. Lead data governance, metadata management, lineage, access controls, and data quality. Partner with stakeholders to translate business needs into scalable, secure, and high-performance data infrastructure, and evaluate emerging technologies to evolve the data platform.

Design, build, and maintain ETL pipelines, databases, and data models to process healthcare claims and related data. Ensure data quality, analyze claims data for trends, and produce reports and visualizations for nontechnical stakeholders. Collaborate with policy and analytics teams to translate requirements into data solutions and improve data infrastructure and analytics capabilities.