Top Entry Level Data Engineer Jobs in NYC, NY

Build the company’s foundational data platform, including ingestion pipelines, record matching, reconciliation tools, and a flexible data warehouse. Partner with the CTO to establish the core system of record and automate customer onboarding. Translate complex data concepts for nontechnical stakeholders while shaping product direction in an early-stage environment. Bonus responsibilities include integrating AI model services and supporting business intelligence dashboards and visual reporting.

Develops data acquisition strategies, integrations, migrations, workflows, architectures, and data management procedures. Builds and maintains data pipelines, ensures data quality and security, supports cloud and healthcare claims data environments, and prepares data for analytics, machine learning, and predictive modeling. Collaborates with business and technical stakeholders to define data standards, requirements, monitoring capabilities, and process improvements.

Design, build, and optimize scalable data pipelines using Databricks, PySpark, SQL, Delta Lake, and lakehouse architecture. Collaborate with analytics, product, and engineering teams while developing batch and streaming workflows, integrating data warehouse systems, and using cloud platforms and orchestration tools such as Azure, Airflow, ADF, or Prefect.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Build and scale distributed data pipelines and architectures supporting AI products and research. Responsibilities include processing large datasets with Spark, designing AWS data systems, managing SQL and NoSQL storage, writing Python and SQL, ensuring data quality and reliability, and implementing automation, orchestration, monitoring, and scalable data operations.

The Associate Data Engineer role is listed with KKR, but the provided description contains only company information and equal-opportunity notices. No specific responsibilities, technologies, qualifications, salary, travel, or clearance requirements are provided.

Design and implement scalable data infrastructure, pipelines, integrations, and models. Build automated insurance reporting pipelines, unify siloed datasets, enable self-service analytics, and enforce governance, quality, security, testing, and reliability standards. Partner with Risk, analytics, product, and cross-functional teams to deliver data solutions using modern data engineering tools and practices.

Builds and maintains data pipelines and datasets supporting analytics, AI, and modeling. Responsibilities include ingesting healthcare claims and lab data into Snowflake and AWS Iceberg, processing structured and unstructured sources, curating data for AI use cases, implementing medallion architectures, evaluating data quality, and contributing to pilot initiatives using SQL, Python, AWS Glue, Terraform, and Snowflake.

Own end-to-end data engineering for a large-scale analytics and machine learning platform. Build ingestion, modeling, transformation, and workflow pipelines; integrate external systems; ensure reliability, scalability, and data quality; and support production machine learning workflows. Develop programmatic interfaces for AI applications and manage analytics engineering within a cloud data warehouse.

Lead delivery for analytics and data platform initiatives as a hands-on player-coach. Build and refine Bronze-to-Silver-to-Gold Medallion transformations, develop performant data models, improve pipeline reliability, and implement complex Spark and Databricks solutions. Coordinate contractors, plan sprints, review pull requests, manage dependencies, and communicate progress and risks. Support a GDPR-first, multi-region data strategy and collaborate with engineering leadership on data warehouse unification.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

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.

Own Qualitate’s data engineering foundation by building pipelines for structured and unstructured data, designing relational and non-relational data models, and developing AI-powered enrichment workflows. Establish data quality monitoring, integrations, schemas, contracts, lineage, and documentation. Partner with Applied AI, Research, and Engineering while shaping the company’s early data architecture and enabling customer data integrations.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

Own Modal’s go-to-market data platform, including organization and people graphs, identity resolution, CRM schemas, Snowflake and dbt models, reverse ETL pipelines, revenue operations metrics, forecasting, attribution, and data quality systems. Partner with Data, RevOps, and Marketing to support customer context, routing, territory management, campaigns, reporting, and reliable GTM workflows.

Build and maintain data pipelines for network and operational telemetry, integrate DataMiner and other monitoring systems, create Power BI dashboards, configure monitoring metrics and alerts, automate reporting, analyze network performance and recovery events, and translate technical findings into actionable insights for technical and executive stakeholders.

Build and maintain production data ingestion pipelines, transformation models, warehouses, and schemas for PerfectServe’s data platform. Deploy reliable, tested pipelines using idempotency and data-quality checks, apply dimensional and medallion modeling, participate in design reviews, and document owned pipelines and runbooks. The role requires SQL, programming experience, dbt or similar transformation tools, cloud data warehouse experience, ELT orchestration, AWS knowledge, and a strong data-testing mindset.

Lead the design and development of Azure-based big data solutions, including batch and real-time ETL/ELT pipelines, data architecture, ingestion, curation, warehousing, and storage. Build solutions using Azure Data Factory, Databricks, Synapse, Spark, Python, SQL, and related Azure services. Support Salesforce migration and integration while applying strong RDBMS, data management, and big data design expertise.

Build and maintain Google Cloud data pipelines and ETL/ELT processes supporting GenAI and RAG solutions. Classify and govern sensitive data using Dataplex and Cloud DLP, enforce access controls, assess AI readiness, and design reusable schemas and data models. Collaborate with engineering, delivery, and client teams to integrate legacy systems, monitor pipeline reliability, conduct data quality testing, document data flows, and proactively identify compliance and delivery risks.

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.

Build and maintain full-stack data engineering solutions supporting BioIntelliSense’s remote patient monitoring and clinical intelligence platform. The role likely involves processing health-monitoring data, enabling analytics, and supporting reporting for clinicians, patients, and employers. Specific responsibilities and qualifications are not provided in the description.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

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.

The Data Engineer will clean, manage, and analyze datasets, automate ETL processes, and collaborate with traders and software engineers to derive insights.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Develop reliable datasets supporting reporting, analysis, finance, revenue recognition, and company decision-making. Collaborate with Finance, Engineering, Sales, Product, and Marketing to understand data needs, improve manual workflows, validate data accuracy, and create scalable solutions using modern data tools.