Top Data Engineer Jobs in NYC, NY

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.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Build, optimize, and maintain scalable data pipelines and reusable datasets for business intelligence, marketing, and data science. Design a federated data validation framework, optimize queries, support data modeling and governance, and protect user privacy and security. The role uses Python, SQL, AWS, Airflow, Snowflake, PostgreSQL, and other modern data-stack technologies, with a hybrid schedule requiring three days weekly in the New York City office.

Develop and advance the enterprise data platform by gathering business requirements, designing data models and warehouses, and building data visualization solutions. Administer reporting, analytics, and visualization platforms while promoting best practices. Collaborate cross-functionally, apply business and process knowledge, manage multiple projects, communicate strategically, and provide technical leadership in data engineering and business intelligence.

Build and maintain dbt data models and pipelines, implement real-time ingestion with PySpark and AWS Glue, and apply software engineering practices to analytics development. Collaborate with data scientists, stakeholders, IT teams, and data stewards on data quality, governance, integrations, and deployment. Translate requirements into Jira stories, manage milestones, review Git pull requests, and support enterprise data activation and advanced analytics.

Develop and maintain scalable data pipelines and secure infrastructure for multimodal synthetic data, AI evaluation, real-time model monitoring, and clinical research data. Responsibilities include data transformation, preprocessing, quality assurance, database management, privacy compliance, interactive dashboards, pipeline documentation, and scientific dissemination.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Build and maintain dbt data models and pipelines, implement real-time ingestion with PySpark and AWS Glue, and process distributed data in Databricks. Apply software engineering practices, review Git pull requests, partner with data scientists and stakeholders, translate requirements into Jira stories, and support data governance, quality, integrations, and deployment across the enterprise.

Define and govern enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, and AI use cases. Establish standards for data quality, lineage, access, cataloging, governance, observability, and SLAs. Architect AI-enabled workflows, resolve complex architecture issues, influence roadmaps, and mentor engineers through hands-on technical leadership. The role requires 15+ years of software, data engineering, or architecture experience and expertise in large-scale data platforms and modeling.

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, Finance, and Infrastructure to evolve data architecture, optimize performance, implement infrastructure-as-code, and ensure reliable, traceable data for AI-powered tools.

Lead Data Engineer responsible for designing, developing, testing, implementing, and supporting cloud-based data and full-stack solutions. The role uses Java, Scala, Python, databases, distributed computing, streaming technologies, and cloud data warehouses. Responsibilities include collaborating across Agile teams, developing scalable solutions, conducting unit tests and code reviews, optimizing performance, keeping current with emerging technologies, and mentoring engineers.

Owns the design, development, operation, and governance of data pipelines and integration patterns supporting Medical Affairs AI products. Builds APIs and ETL/ELT solutions connecting enterprise systems to analytics platforms, RAG pipelines, vector databases, and GraphRAG applications. Ensures data quality, privacy, lineage, compliance, and protection of sensitive information. Partners with architecture, engineering, product, and business stakeholders to deliver reusable, production-grade data integrations.

Leads data engineering initiatives by designing, developing, testing, implementing, and supporting cloud-based data solutions. Collaborates with Agile teams, product managers, and machine learning engineers; works with distributed systems, databases, streaming applications, and data warehouses. Performs code reviews and unit testing, optimizes performance, explores emerging technologies, and mentors engineering team members.

Build and maintain recruiting analytics data infrastructure using BigQuery, dbt, Fivetran, SQL, and Python. Design dimensional models, ETL/ELT pipelines, semantic layers, governance controls, data security, monitoring, and standardized recruiting metrics. Integrate Workday, Greenhouse, and internal systems while supporting batch and real-time analytics. Partner with data scientists, engineers, recruiters, and business stakeholders to deliver trusted data products and AI-enabled workforce insights.

Build and operate production clinical-trial data pipelines using Python, PySpark, and SQL. Transform diverse source data into internal models, develop quality checks and reconciliation, investigate data issues, and improve observability. Partner with clinical subject matter experts and customer-facing teams to translate domain knowledge into reliable pipeline logic. Deliver customer-driven changes under enterprise SLAs while maintaining engineering standards through testing, code review, documentation, and CI/CD.

Design and scale lakehouse architecture, including Bronze, Silver, and Gold data layers. Build reliable streaming and batch pipelines with Kafka, Spark, and Airflow; manage Iceberg, Delta, and Hudi table formats; optimize Trino, Starburst, and Databricks queries; and improve platform reliability, data quality, and performance. Collaborate with data scientists, analysts, and product teams to deliver scalable data platform solutions.

Design and scale lakehouse architecture, streaming and batch pipelines, and reliable data platforms using Kafka, Spark, Airflow, Iceberg, Databricks, and related technologies. Build Medallion-layer data systems, manage open table formats, optimize distributed queries, monitor platform reliability, resolve data quality issues, and collaborate with analysts, data scientists, and product teams.

Build and maintain scalable ETL/ELT pipelines and data foundations using Databricks, dbt, and AWS. Transform raw structured and unstructured data into reliable datasets for product analytics and data science, including telemetry, streaming data, and feature engineering. Develop dashboards in Tableau and Databricks, optimize workflows, and collaborate with Product, Engineering, analytics, and data science stakeholders to support product health, adoption, and performance insights.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Design and deliver enterprise-scale data models and analytics-ready datasets in Snowflake; build reliable ELT/ETL pipelines (batch and near-real-time); enforce data quality, observability, and secure handling of PII; optimize performance and publish curated data to downstream systems while collaborating with product, operations, and engineering teams.

Staff Data Engineer will set technical direction and build Eve’s scalable data platform. Responsibilities include Snowflake administration, role-based access, governance, ingestion, dbt modeling and orchestration, Terraform infrastructure, observability, schema change detection, reliability practices, and CI standards. The role also mentors engineers and analysts, supports AI-assisted development, and establishes secure, reliable workflows for product, case, customer, and go-to-market data.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.