Top Data & Analytics Jobs

Lead data engineering strategy and develop enterprise data lake solutions on AWS. Build data pipelines using AWS data integration, engineering, and analytics resources, develop API services with FastAPI or Flask, optimize engineering practices, create long-term data plans, and represent the team in executive meetings.

Lead architecture and build scalable data pipelines and ETL for large datasets, optimize data models and DB structures, ensure data quality and governance, work with cloud platforms (Snowflake/AWS), mentor junior engineers, collaborate with data scientists and product teams, and apply geospatial and big-data technologies to deliver data-driven transportation solutions.

Build and own production-grade data pipelines, source connectors, warehouse transformations, curated models, reconciliation, lineage, and data quality systems. Design scalable dbt projects and orchestration workflows using Airflow or Dagster, optimize warehouse performance, integrate messy enterprise systems, and ensure pipelines are reliable, idempotent, auditable, and cost-effective. Collaborate with backend and AI teams, establish data contracts, review work, and mentor engineers.

Build and maintain dbt and semantic data models in Snowflake, develop ingestion and transformation pipelines, define trusted product metrics, and improve data collection. Partner with Product and Engineering to support experimentation and feature analysis. Establish data quality testing, documentation, HIPAA-aware access controls, and compliance practices. Use and validate AI-generated code and Snowflake Cortex outputs while raising technical standards across the team.

Build and maintain scalable data pipelines, models, and cloud infrastructure supporting Xometry’s global marketplace. Collaborate with data scientists, ML engineers, product teams, and business stakeholders to translate requirements into reliable data solutions. Develop self-service tooling, optimize Snowflake and AWS environments, support CI/CD deployment processes, and ensure data integrity, consistency, and usability across internal and external consumers.

Build and maintain scalable data pipelines, models, and cloud infrastructure supporting business performance analysis. Collaborate with data scientists, ML engineers, product teams, and business stakeholders to translate requirements into technical solutions. Develop self-service data tooling, optimize Snowflake and AWS environments for scale and cost, and support deployment through CI/CD and DevOps practices. The role also involves data integration, modeling, documentation, and ensuring data integrity across global entities.

Builds and maintains scalable data pipelines and reusable datasets in Databricks. Ingests, validates, transforms, and models structured and unstructured data using Python, PySpark, and SQL. Integrates warehouse and cloud analytics systems, supports Tableau and Spotfire reporting, improves reliability and performance, and implements testing, monitoring, documentation, and CI/CD. Collaborates with technical and business stakeholders while providing technical guidance, code reviews, and mentorship.

Designs scalable data architectures and pipelines supporting digital twins, analytics, and operational modeling. Develops a digital twin of operating-room environments, translates workflows into data and simulation models, and builds scenario-analysis capabilities for staffing, scheduling, capacity, patient flow, and resource utilization. Creates dashboards, APIs, and decision-support tools while establishing data governance, quality, lineage, validation, documentation, and monitoring. Collaborates with healthcare stakeholders, technical teams, and government leadership.

Leads the design, development, and implementation of scalable data pipelines, APIs, cloud-based integrations, and Customer Data Platform solutions. Builds unified customer data ecosystems supporting analytics, personalization, and enterprise integration. Responsibilities include data modeling, Lakehouse and API optimization, governance, privacy compliance, CI/CD automation, monitoring, troubleshooting, documentation, and cross-functional collaboration. Requires extensive experience with Python, SQL, REST APIs, cloud platforms, data integration tools, CDPs, and modern data engineering architectures.

Lead enterprise data strategy and delivery across data platforms, master data management, semantic layers, governance, quality, modeling, and pipelines. The role requires hands-on architecture leadership, executive stakeholder engagement, and delivery of complex multi-region platforms using Snowflake, cloud infrastructure, MDM, Collibra, dbt, and batch and streaming architectures.

Design, build, and operate big-data and streaming platforms (Kafka, Hadoop, Dremio). Develop, deploy, and monitor data pipelines using Java, Python, Spark/Flink; collaborate on data modeling and ingestion, ensure data integrity and availability, act as a Big Data SME, and perform root-cause analysis.

Develop and optimize enterprise Oracle SQL and PL/SQL solutions, including data models, database designs, APIs, ETL processes, batch jobs, and performance tuning. Collaborate with cross-functional teams to translate requirements into scalable implementations, support testing and CI/CD, troubleshoot production issues, and document solutions. Provide technical leadership, mentor engineers, participate in architecture and code reviews, and drive engineering best practices across data modernization initiatives.

Participate in AHEAD’s associate development program, completing technical training, certifications, and project shadowing before supporting client data solutions projects. Responsibilities include collaborating across teams, solving complex problems, managing priorities, meeting deadlines, and developing consulting expertise. Candidates should have programming experience, SQL or NoSQL knowledge, willingness to travel extensively, and existing U.S. work authorization. Data platform and cloud experience are preferred.

Design, build, and maintain scalable batch and streaming data pipelines. Write clean code (Python, SQL, Spark), ensure data quality and reliability, monitor and troubleshoot pipelines, document sources and transformations, and collaborate with analysts, data scientists, and product teams to deliver data products and support analytics and ML use cases.

Supports data pipeline development and maintenance for a VA disability claims program. Responsibilities include preparing and cleansing healthcare data, troubleshooting ETL/ELT pipelines, performing data quality checks, documenting workflows, participating in code reviews, and learning claims data systems under senior engineer mentorship.

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.

Build and operate data pipelines that integrate customer financial data from ERPs, warehouses, and payment systems. Develop AI agents that automate ERP implementations, partner with implementation teams to understand and transform customer data, and contribute to shared AI platform capabilities. The role requires strong full-stack engineering fundamentals, autonomous execution, and comfort working in an ambiguous, fast-moving environment.

Design, build, and operate scalable ETL/ELT pipelines and backend data services using Go. Integrate relational, NoSQL, blockchain, API, web, and social data sources; develop NLP pipelines for text extraction and classification; and support production APIs, dashboards, alerts, and intelligence systems. Collaborate with engineering, data science, and investigations teams on architecture, data quality, lineage, validation, monitoring, and client-facing products.

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.

Design, build, and maintain scalable batch and streaming data pipelines using Python, SQL, Kafka, and GCP. Develop REST and GraphQL data services, CI/CD automation, observability, and data-quality frameworks. Integrate ML and NLP models into production workflows, including continuous retraining and real-time inference. Support structured and unstructured healthcare datasets, distributed systems, microservices, and reliable data infrastructure while collaborating with data science and MLOps teams.

Supports cybersecurity data-source onboarding and maintains Cribl and Databricks pipelines for collection, transformation, enrichment, normalization, validation, and delivery. Develops Python and SQL automation, troubleshoots ingestion and schema issues, monitors pipeline performance, documents data flows and procedures, and supports integrations with security and analytics platforms. Collaborates with engineers, analysts, data scientists, and mission stakeholders in classified and unclassified environments.

Designs and manages an enterprise data foundation supporting AI, analytics, and cloud applications. Responsibilities include building AWS ingestion and integration pipelines, managing S3 data lakes, developing QuickSight datasets, preparing data for Bedrock and RAG solutions, maintaining vector databases, and implementing metadata, lineage, governance, quality, and security controls. The role also develops APIs, supports SaaS application integrations, optimizes cloud storage, and collaborates with engineering, security, AI, and business intelligence teams.

Designs and maintains scalable data pipelines, warehouses, lakes, integration solutions, and analytics platforms. Develops AWS and Snowflake data solutions using Python, SQL, dbt, ETL/ELT, and Data Vault modeling. Supports replication, APIs, governance, metadata, lineage, and data quality across enterprise environments. Troubleshoots complex data issues and provides technical leadership while collaborating with engineering, architecture, analytics, and business teams.

Supports the development and maintenance of data pipelines, models, ETL workflows, and data quality processes for marketing analytics and CRM solutions. Writes Python, SQL, or Scala code, monitors and troubleshoots data flows, integrates data from multiple sources, and collaborates with engineers, analysts, data scientists, CRM developers, and product owners. Learns cloud data architecture, migrations, and modern data management practices.

Designs and maintains scalable data pipelines and architectures for operational, intelligence, and mission data. Aggregates, cleans, transforms, and analyzes structured and unstructured datasets using Python, Maven Smart System, Palantir, Power BI, and AI-enabled tools. Develops dashboards, data models, metrics, reports, and decision-support products for C-UAS operations and acquisition efforts. Requires collaboration with technical and mission stakeholders, strong data governance practices, executive communication skills, and an active TS/SCI clearance.