Top Data & Analytics Jobs

Build and operate production-grade AML data products, ETL pipelines, data models, integrations, and ML-ready datasets. Ensure data quality, lineage, security, governance, observability, reliability, and regulatory compliance. Support transaction monitoring, customer segmentation, risk rating, sanctions screening, and financial crime prevention initiatives. Lead architecture decisions, production support, incident response, CI/CD improvements, automated testing, code reviews, mentoring, and engineering standards.

Designs and maintains ELT pipelines, warehouse integrations, data models, and data quality processes. Collaborates with data scientists, analysts, engineers, and stakeholders to deliver scalable analytical solutions. Responsibilities include data integration, governance, security, performance optimization, exploratory analysis, visualization, and cloud-based API ingestion. Requires strong SQL, DBT, Snowflake, Azure Data Factory, cloud, and scripting experience, plus leadership and communication skills.

Design, implement, and optimize large-scale data ingestion, cleansing, transformation, loading, replication, and display systems. Develop data pipelines, profiling, deduplication, matching logic, and custom data-source solutions. Maintain data-layer documentation, collaborate with data scientists on AI/ML workflows, and use Java technologies, REST APIs, Airflow, ETL, SQL, and Linux in a Big Data environment.

Design, build, and maintain batch and streaming data pipelines; ingest, transform, and optimize data for analytics and BI; ensure data quality, reliability, and performance; implement monitoring/alerting; support MLOps and analytics workloads; collaborate with data scientists and BI teams; document AI systems and RAG/Cortex-based solutions.

Develops scalable data and software systems using Python and Apache Spark. Designs APIs, data models, ETL workflows, orchestration, and data interactions while optimizing performance, scalability, integrity, and security. Builds user interfaces, contributes to automated testing, applies Agile practices, and supports CI/CD delivery. The role requires strong software engineering, distributed computing, analytical, and communication skills, with AWS, R, Databricks, and SAS experience preferred.

Design and scale reliable data platforms, pipelines, Java microservices, APIs, and reporting infrastructure. Build data validation and reconciliation workflows, support distributed systems, improve pipeline performance and resiliency, and partner with engineering, analytics, and business teams. Contribute to cloud-native infrastructure, CI/CD, Docker, Kubernetes, dashboards, and self-service reporting modernization.

Own Fullbay’s Snowflake data platform end to end, including dimensional modeling, certified metric definitions, semantic layers, identity resolution, transformations, governance, security, cost management, source ingestion, and AI-agent enablement. Build tested SQL and transformation code, establish company-wide data standards, document systems thoroughly, and direct an architecture consultant. Provide guidance for self-service data use while serving as the final authority on definitions, access, and platform design.

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Collaborates with data scientists, engineers, and mission stakeholders while applying data validation, modeling, quality, testing, documentation, and Agile practices. The role requires onsite work in a SCIF and support for classified national security missions.

Maintain and troubleshoot Airtable systems and API integrations connecting Salesforce, Coupa, AWS finance tools, and operational data sources. Support automated reporting, dashboards, executive performance reports, data validation, documentation, naming standards, and metadata governance. The role focuses on testing, reliability, issue resolution, and onsite support, with mentorship from a Senior Data Operations Architect.

Maintain and troubleshoot Airtable systems, API integrations, dashboards, and automated reporting across financial, moderation, and production data. Validate integrations, resolve data-flow issues, maintain data standards and documentation, and support operational data reliability. The role is fully onsite and provides mentorship from a Senior Data Operations Architect.

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.

Technical lead for Finance Technology data engineering: design and build scalable batch and real-time data pipelines (Hadoop/Spark/PySpark/Scala/Java), optimize data models and APIs, drive cloud-based data architecture (AWS/GCP/Azure), enforce governance/security, and mentor engineers to deliver enterprise financial data services.

Own Double E’s data lake and improve data integrity by building ETL pipelines, data models, integrations, and reporting solutions. Manage Azure SQL and Power BI, consolidate ERP and CRM data across global offices, establish governance and documentation standards, and translate business needs into reliable, scalable data infrastructure. The role partners with technical and business stakeholders and supports modernization, system connectivity, and analytics initiatives.

Own and evolve Later’s data platform across ingestion, orchestration, storage, transformation, and consumption. Build reliable batch and real-time pipelines, backend services, APIs, infrastructure as code, CI/CD automation, observability, data quality controls, and secure GCP-AWS data movement. Modernize BigQuery and lakehouse infrastructure, improve performance and cost, mentor engineers, and establish governance, metadata, lineage, and freshness foundations for analytics, machine learning, LLMs, and AI agents.

The Senior Data Engineer designs and maintains production data pipelines on GCP using BigQuery, Dataform, Airflow, Dataflow, and Cloud Run. They own data validation, cleansing, troubleshooting, API integrations, and reliable datasets for analytics and AI/ML. The role also involves translating client requirements into data solutions, maintaining dashboards, supporting ad hoc requests, and applying Git, code reviews, and CI/CD practices.

Build and operate reliable ETL and ELT pipelines across lakehouse and transactional systems. Develop data models, enforce governance, integrate diverse data sources, and support analytics, APIs, and predictive tools. Monitor performance, data quality, lineage, cost, and uptime while collaborating with data science, AI, product, and innovation teams.

Supports the Fraud Data Engineering team through data integration, quality validation, analytics, documentation, and platform modernization. Responsibilities include gathering requirements, creating business requirement documents and source-to-target mappings, enabling batch and real-time integrations, analyzing large datasets, documenting data flows, participating in Agile activities, and developing Databricks notebooks and validation frameworks. The role is a 12-week internship focused on fraud and security data solutions.

Supports enterprise data engineering initiatives involving real-time event streaming, Voice of the Member analytics, master data management, data quality, metadata, and AI technology proofs of concept. The intern analyzes data flows and feedback, assists with platform governance and automation, researches technology trends, collaborates with engineers and stakeholders, and presents findings. The 12-week program requires current enrollment in a technical degree program with graduation in December 2027 or later.

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.

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 scalable healthcare data pipelines, dbt models, event-driven services, APIs, and Google Cloud infrastructure. Model and govern multimodal EHR, genomic, and imaging data for AI agents across federated hospital networks. Ensure data quality, lineage, observability, security, HIPAA compliance, and production reliability while contributing to TypeScript application services and agentic workflows.

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.

Leads customer-facing enterprise data engineering engagements for federal clients, designing scalable data architectures, pipelines, integrations, APIs, object storage solutions, and Java-based applications. Responsibilities include architecture workshops, technical implementation, troubleshooting, documentation, customer communication, mentoring junior engineers, and service innovation. The role requires active Top Secret clearance, at least eight years of relevant experience, strong consulting skills, and expertise across enterprise software, data engineering, and integration technologies.

Designs, builds, and maintains enterprise data pipelines, ETL processes, integrations, cloud data infrastructure, and complex data models. Supports analytics partners, develops data warehouse solutions, tunes SQL and database performance, troubleshoots production issues, and delivers end-to-end data engineering projects. The role collaborates with architects, analysts, developers, and business stakeholders, applies big data technologies, supports agile delivery, and mentors team members.