Top Data Engineer Jobs in NYC, NY

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.

Build and maintain Databricks-based data platforms, including ingestion, transformation, storage, governance, data modeling, and serving pipelines. Establish medallion architecture standards, canonical data models, quality controls, lineage, schema evolution, and reliable batch or incremental processing. Improve pipeline observability, scalability, idempotency, and recoverability while moving curated data to systems such as ClickHouse. Collaborate across application and analytics teams to create durable, governed production datasets.

Build and maintain data pipelines, reporting solutions, Power BI dashboards, semantic models, and KPIs. Clean, transform, validate, and reconcile data from ERP, Microsoft, Excel, third-party, and operational sources. Automate repetitive reporting, troubleshoot data issues, support recurring and ad hoc requests, document business logic, and collaborate with departments to deliver reliable reporting and process improvements.

Lead data engineering efforts by designing, developing, testing, and supporting scalable data pipelines and movement solutions. Build cloud-based systems using Python, SQL, Spark, Kafka, Snowflake, and Databricks; optimize data models, quality, governance, and performance; collaborate with Agile teams and product managers; conduct code reviews and testing; and mentor engineers while applying emerging technologies and interactive AI tools.

Build, optimize, and maintain scalable data pipelines and reusable datasets for business intelligence, marketing, and data science. Develop federated data validation frameworks, optimize queries and data models, and protect user privacy and security. The role uses Python, SQL, AWS, Airflow, PostgreSQL, Iceberg, Snowflake, Spark, and other modern data-stack technologies. It is based in New York City with in-office work three days per week.

Own and maintain dbt data models across design, testing, deployment, monitoring, and documentation. Translate ambiguous Marketing and Product needs into reliable data solutions, write design documents, support production issues, create runbooks and schema documentation, and coach junior engineers. The role uses SQL, Python, Snowflake, dbt, Dagster, and AWS while partnering closely with Marketing, Data, and MarTech teams.

Build and govern Anthropic’s quote-to-cash data foundation. Design canonical models from Salesforce, CPQ, and billing data; establish data integrity standards and SLAs; collaborate with systems engineers on schema changes and ingestion; and create data products, dashboards, and self-service analytics tools for GTM and Finance teams. The role also owns data architecture, translates stakeholder needs into technical requirements, and influences roadmaps.

Designs, builds, and maintains data infrastructure, ETL pipelines, data models, and analytics data warehouses. Ensures accurate data flow from creation through presentation layers and supports reporting, analytics, and machine learning initiatives. Collaborates with stakeholders to deliver data products, improves engineering tooling and pipelines, participates in on-call support, and uses cloud, containerization, orchestration, streaming, and CI/CD technologies.

Design, build, and operate Azure-based data pipelines and integrations. Consolidate data from multiple sources into centralized SQL systems, perform transformations and field mapping, and maintain data warehouse capabilities. Troubleshoot quality, integrity, latency, security, and reliability issues while applying governance, monitoring, and SLA practices. Collaborate with engineers, analysts, and stakeholders in Agile pods to deliver reusable, analytics-ready data assets.

Leads technical strategy for enterprise data pipelines and data-sharing platforms. Builds scalable, resilient, high-performance systems using AWS, lakehouse architecture, Kafka, Flink, Spark, Snowflake, and Databricks. Develops code, drives engineering excellence and technology adoption, influences enterprise stakeholders, advises on platform capabilities, mentors engineers, and recruits technical talent. The role requires deep data engineering and architecture expertise, hands-on leadership, and innovation across internal and external data environments.

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.

Designs, builds, tests, and documents scalable data solutions and pipelines for CMS healthcare fraud, waste, and abuse initiatives. Responsibilities include data warehouse development, CI/CD deployment, orchestration, data quality, governance, AWS-based automation, testing, architecture support, and technical leadership. The role partners with analysts and business stakeholders, leads project initiatives, improves workflow automation, and ensures requirements and milestones are met.

Build and support scalable data platforms and pipelines, leading migrations such as Snowflake to BigQuery and transitioning reporting to Looker. Responsibilities include data architecture, ETL/ELT, API and marketing integrations, data quality, production troubleshooting, warehousing, performance optimization, and platform modernization. The role partners with analytics and business teams, owns projects through production, documents solutions, and provides technical guidance.

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).

Develop and maintain data engineering solutions using Informatica Data Management Cloud, Control-M, Snowflake, and SQL. Responsibilities include sourcing, transforming, cleansing, ingesting, analyzing, and visualizing data; building automated workflows and scalable ETL processes; supporting data architecture and deployments; and delivering reusable code for large datasets. The role requires collaboration with technical and business partners, troubleshooting, mentoring, and managing multiple priorities.

Design and lead scalable, secure, highly available database infrastructure for multi-terabyte SQL Server environments. Set technical direction for data architecture, performance, real-time pipelines, security, and growth. Troubleshoot complex production issues, optimize queries and indexes, guide cross-functional projects, mentor engineers, review changes, reduce technical debt, and evaluate emerging technologies. Monitor database health using Splunk, Datadog, and Grafana while influencing engineering and Site Reliability strategy.

Develops and maintains data pipelines, datasets, schemas, models, databases, and data flows. Analyzes raw and structured data, improves data quality, builds reporting and data dictionaries, and collaborates with analysts, scientists, and stakeholders. The role supports data security, compliance, visualization, and reporting while modernizing legacy technologies. Requires a flexible schedule supporting 24/7/365 operations and the ability to obtain and maintain a security clearance.

Configure and maintain Adobe Experience Platform and Real-Time CDP data pipelines, XDM schemas, datasets, identity resolution, Profile enablement, segmentation readiness, and destination activation. Validate data quality, troubleshoot ingestion and identity issues, manage sandboxes, and implement privacy, consent, governance, and access controls. Partner with architects, data engineers, consultants, analysts, data scientists, and marketing teams to support reporting, personalization, and machine learning readiness.

Lead development of scalable data pipelines and cloud-based data solutions using Python, SQL, Spark, Kafka, Snowflake, and Databricks. Responsibilities include data modeling, data quality, governance, testing, performance optimization, technical design, collaboration with Agile teams and product managers, and mentoring engineers. The role supports omnichannel marketing and messaging platforms while encouraging adoption of emerging technologies and interactive AI tools.

Own production data ingestion pipelines for healthcare and benefits data, including normalization, mapping, validation, reconciliation, observability, and secure handling of sensitive information. Unify census data processes, scale the platform, improve identity matching, and build automation and TypeScript tooling. The role requires strong Python and SQL skills, production orchestration experience, reliable testing and data-quality practices, and effective communication with technical and nontechnical stakeholders.

Build and maintain Databricks data pipelines, ETL/ELT processes, SQL transformations, and Power BI dashboards for HR and People Analytics. Ensure data quality, optimize models and reporting performance, troubleshoot issues, document solutions, and collaborate with HR stakeholders and technical teams to deliver scalable workforce analytics.

Design, build, and operate high-throughput data pipelines supporting billing and cost attribution. Drive Spark optimization, latency reduction, sharding, cross-datacenter reliability, and new billing capabilities. Investigate billing accuracy issues with Finance and Product teams, participate in on-call rotation, and improve engineering standards and operational excellence.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Design, build, and maintain scalable batch and streaming data pipelines, cloud infrastructure, and the Enterprise Data Hub. Partner with stakeholders to productionize ingestions, ensure data quality, implement metadata/catalog solutions, mentor junior engineers, and support analytics and ML workloads using cloud and big-data tooling.