Top Data & Analytics Jobs

Build and scale data pipelines, APIs, integrations, and data models supporting CRM personalization, segmentation, audience activation, and campaign measurement. Ensure pipeline reliability through monitoring, alerting, and logging; automate workflows; document systems; participate in architecture and code reviews; and mentor junior engineers. Collaborate with data engineering, data science, and CRM teams to improve personalized email, push, and SMS engagement for millions of users.

Designs and maintains scalable data pipelines and platform architecture using Python, dbt, Airflow, and Snowflake. Responsibilities include data modeling, ELT development, quality and observability, production debugging, source-data validation, performance optimization, and Tableau reporting. The role leads ad hoc analysis, collaborates with engineering, marketing, product, and leadership teams, makes architectural decisions, and mentors colleagues through code reviews and knowledge sharing.

Build and maintain scalable data pipelines, analytical data models, backend data systems, business rules, metrics, and data sources. Partner with teams across finance, growth, product, customer support, sales, and marketing to support analytical needs and improve data-driven decision-making. Own data engineering problems end to end and help advance organizational data practices.

Data Engineering Interns will define metrics, instrument logging, ingest and model data, transform datasets, ensure data quality and governance, and support visualization, reporting, alerting, and scalable systems. They will partner with product, analytics, and data consumers to improve product engagement, efficiency, costs, and strategic decision-making. The 12-week, full-time internship includes technical training, mentorship, and hands-on project experience.

Design, build, and operate scalable cloud data platforms, pipelines, and applications using distributed data technologies. Lead architectural decisions, data modeling, platform selection, and large-scale transformation initiatives. Collaborate with product managers, software engineers, analysts, and data scientists while ensuring resilience, maintainability, and operational efficiency. Mentor engineers, establish engineering standards, communicate technical outcomes, and independently deliver end-to-end data solutions across relational, NoSQL, batch, and streaming environments.

Lead the design and development of scalable, low-latency real-time data platforms and distributed streaming pipelines. Build data solutions using Flink, Spark, Kafka, Snowflake, AWS services, and related technologies. Guide engineers, establish data architecture and pipeline best practices, develop cloud-based solutions, support Agile delivery, perform testing and code reviews, and mentor team members. The role also involves data modeling, observability, data warehousing, and collaboration with product, security, and engineering teams.

Build and operate governed Finance data infrastructure on Databricks, including scalable pipelines, integrations, semantic layers, data-quality controls, observability, and AI-enabled services. Partner with analytics and enterprise teams on architecture, data contracts, security, recoverability, and production ownership. Optimize platform costs through FinOps practices and use AI coding assistants while validating performance, correctness, and security.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, warehousing, streaming, security, and observability. The engineer collaborates with Agile teams, architects reusable engineering patterns, communicates technical outcomes, mentors colleagues, and delivers resilient data solutions using technologies such as Python, Spark, Databricks, and Snowflake.

Design, build, and architect scalable cloud data platforms, pipelines, and applications. Lead large-scale data initiatives, make architectural decisions, evaluate technologies such as Snowflake and Databricks, enforce engineering patterns, and ensure performance and resilience. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical outcomes to stakeholders.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Collaborate with engineering, product, analytics, and data science teams; implement secure, resilient, reusable data solutions; support distributed and streaming workloads; apply data modeling and observability practices; communicate technical concepts; and mentor other data engineers.

Build and operate scalable cloud data platforms and pipelines for Capital One’s Risk Tech organization. Responsibilities include designing distributed data solutions, enforcing engineering patterns, implementing security controls, collaborating across Agile teams, mentoring developers and analysts, and delivering AI-powered risk management products. The role requires expertise in Python, Spark, SQL, databases, cloud data warehousing, data modeling, and pipeline development.

Build, optimize, and maintain scalable data pipelines and reusable datasets for business intelligence, marketing, and data science. Develop a federated data validation framework, optimize queries and data models, and protect sensitive healthcare data through privacy and security best practices. The role uses Python, SQL, AWS, Airflow, PostgreSQL, Snowflake, and modern data-stack technologies. This position is based in New York City and requires working onsite three days per week.

Build and maintain scalable data pipelines supporting reporting, analytics, and AI/ML use cases. Write production Python and SQL code, integrate data from APIs and other sources, establish data governance, implement quality monitoring and alerting, support production incidents, and document technical solutions. Collaborate with data scientists, engineers, and business partners to deliver reliable data infrastructure and insights.

Build and maintain scalable data models, pipelines, ETL workflows, and streaming solutions using dbt, PySpark, AWS Glue, Databricks, and Kafka. Apply software engineering practices including testing, version control, code reviews, and integration. Partner with data scientists, stakeholders, and data stewards to improve data quality, governance, accessibility, and reliability. Translate requirements into development plans and Jira stories while collaborating on data strategy, tooling, integrations, and deployments.

Build and maintain Databricks data pipelines using SparkSQL and PySpark; ingest, transform, model, and validate healthcare data from payer, provider, platform, and API sources. Support automated file exchanges, dashboards, downstream workflows, testing, governance, access controls, and audit logging. Participate in design and code reviews, use AI coding tools responsibly, and progressively own data engineering work while handling protected health information.

Designs, builds, and operates scalable data pipelines, services, and products supporting clickstream and behavioral data. Responsibilities include data modeling, API and system design, automated quality frameworks, observability, governance, reliability, security, and performance optimization. The role collaborates with engineering, product, analytics, and domain teams, translates complex requirements into production solutions, and applies AI/ML-enabled tools and workflows. It also contributes to technical decisions, data-driven improvements, and operational excellence.

Designs, builds, and operates scalable cloud data platforms, APIs, pipelines, and data products. Leads architectural decisions, evaluates platforms such as Snowflake and Databricks, develops distributed and streaming workloads, enforces engineering standards, and mentors data engineers. Collaborates with product managers, software engineers, analysts, and data scientists to deliver resilient solutions. Requires extensive experience with application development, distributed data, SQL, programming, data pipelines, data modeling, cloud platforms, and relational and NoSQL databases.

Leads development of secure, scalable data platforms for KYC and risk assessment. Designs and delivers enterprise data applications, frameworks, APIs, and processing systems; mentors engineers; drives AI-assisted development practices; and ensures secure, reliable, and compliant engineering workflows. The role uses cloud-native technologies, large-scale data processing, databases, observability, orchestration, modern data platforms, and emerging LLM infrastructure while communicating with senior leaders.

Develop and maintain Java applications, RESTful APIs, backend services, and cloud-ready integrations using AWS. Write optimized SQL, automate tasks with Bash, manage code with Git, troubleshoot production issues, and participate in architecture discussions, code reviews, and continuous improvement. The role requires ownership of end-to-end technical solutions and collaboration across teams.

Design, build, test, and support scalable cloud data solutions and pipelines. Develop data platforms using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases, and cloud technologies. Establish reusable data engineering patterns, security controls, and operational standards. Collaborate with product managers, engineers, analysts, and data scientists; communicate technical outcomes; mentor data professionals; and deliver resilient, high-performance financial data products.

Build and maintain Databricks data pipelines using SparkSQL and PySpark, integrating payer, provider, claims, platform, and API data. Develop transformations, models, file exchanges, dashboards, automated workflows, tests, and data-quality validations. Collaborate in design and code reviews, use AI coding tools responsibly, and protect PHI through governance, access controls, and audit logging.

Design, build, and operate scalable cloud data pipelines, platforms, and applications using distributed data technologies. Lead architectural decisions, evaluate platforms such as Snowflake and Databricks, establish engineering patterns, and deliver resilient data solutions. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring engineers and communicating technical outcomes to stakeholders. Drive large-scale data initiatives independently in an Agile environment.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Collaborate with engineering, product, analytics, and data science teams; develop distributed and real-time data workloads; enforce reusable engineering patterns; implement security and access controls; optimize resilience and operational efficiency; communicate technical outcomes; and mentor data professionals.

Build and maintain production data pipelines, integrations, semantic models, AI agents, copilots, and automated workflows using business data. Partner with Sales, Marketing, RevOps, and Customer Success to deliver reliable analytics and automation solutions. Establish data quality, monitoring, evaluation, access controls, and deployment practices while enabling colleagues to adopt AI tools effectively.

Lead the design and development of scalable data pipelines, microservices, cloud-native ingestion systems, and streaming solutions. Collaborate with architects, engineers, product teams, and business stakeholders to build reliable data platforms using Python, Java, AWS services, SQL, and relational and NoSQL databases. Establish data quality, testing, monitoring, automation, and validation frameworks while optimizing performance and resolving production issues across distributed systems.