Top Data & Analytics Jobs

Design, build, and maintain scalable ETL/data pipelines and an authoritative Lakehouse using Databricks and cloud platforms. Collaborate on data platform strategy, implement CI/CD, model data, refresh dashboards, apply advanced analytics (LLMs/AI) to unstructured data, and support post-award reporting to improve grant program outcomes.

The DMS Data Engineer is responsible for analyzing raw data, building data systems and pipelines, conducting complex data analyses, and collaborating on projects to enhance data quality and reliability.

Design, build, test, and operationalize data pipelines and solutions using cloud technologies. Ensure data quality, governance, and security; analyze sources; resolve defects; collaborate across teams; support AI/ML and BI use cases; and document and educate users on data products.

Design, build, and maintain petabyte-scale ETL pipelines and data platforms using Spark (PySpark/Java), AWS, and Kafka. Model complex data structures, convert varied data formats, operationalize and monitor scalable data applications, and lead cross-functional teams to support mission-driven analytics. Must meet TS/SCI with polygraph requirements.

Design, build, and maintain data pipelines and platforms to ingest, prepare, and organize structured and unstructured data. Develop ETL using Java/Python/SQL/Scala, work with PostgreSQL or Kafka, and leverage AWS services. Collaborate with cross-functional teams in an agile environment and support real-time/streaming applications while meeting security and clearance requirements.

The Data Engineer will design and develop data pipelines for quality monitoring, assess data integrity, and build web applications supporting user validation and analysis.

Build and maintain ETL/ELT pipelines in a lakehouse/cloud environment, ensure data quality and lineage, and apply classical statistics and ML (anomaly detection, regression, time-series) to support operational decision-making and stakeholder reporting.

Design, develop, and deploy end-to-end real-time streaming and ETL/ELT solutions on AWS (including geospatial pipelines). Build APIs, implement infrastructure-as-code, ensure security/compliance, and move systems to production and O&M for high‑scale distributed environments.

The role involves building and maintaining AI data capabilities, implementing data pipelines, and ensuring operational excellence in data management within a healthcare context.

Designs, builds, and maintains scalable batch and streaming data pipelines, ETL/ELT workflows, data models, and Python transformations. Writes optimized SQL, monitors pipeline reliability, troubleshoots failures, implements data-quality validation, and supports security and governance practices. Collaborates with engineers, analysts, stakeholders, and clients to define data contracts, improve platforms, document lineage, and deliver reliable analytics data.

Administer, maintain, and optimize SQL and NoSQL database environments; apply data warehousing concepts; perform data management and maintenance using SQL, Python, and Apache Spark; support cloud database infrastructure with Git, AWS, or Azure; and provide database support for Tableau and Power BI reporting.

Administer, maintain, and optimize SQL and NoSQL database environments. Apply data warehousing and data management practices, using SQL, Python, and Apache Spark. Support database infrastructure with Git and AWS or Azure, and provide database support for Tableau and Power BI reporting and visualization tools.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

Design, build, and maintain large-scale data pipelines and DAGs (Airflow) that process imagery, LiDAR, and other sensor data. Improve platform reliability, observability, resource tuning, and Kubernetes deployments. Collaborate with ML, hardware, and full-stack teams; own pipeline design, implementation, testing, and operations in a hybrid NYC role.

Lead Data Engineer responsible for designing, developing, testing, and supporting cloud-based data solutions for Risk Tech. Collaborate with Agile teams to build distributed, real-time data and streaming systems using big data tools, data warehouses, RDBMS/NoSQL, and mentor engineering peers while ensuring performance and code quality.

Lead and mentor a data engineering team to design, develop, test, and support cloud-based big data and streaming solutions. Build full-stack data pipelines using Java/Scala/Python, RDBMS/NoSQL, Redshift/Snowflake, and cloud platforms. Collaborate with product managers, perform unit tests and code reviews, and promote engineering best practices and tech community engagement.

The Cloud Data Engineer designs and supports scalable Snowflake and Azure data architectures, environments, and ETL/ELT pipelines. Responsibilities include Snowflake performance and cost optimization, Azure resource management, security controls, troubleshooting, documentation, design reviews, and collaboration with architecture, engineering, BI, and security teams. The role also contributes to software development, testing, debugging, mentoring, and data warehouse and data mart implementation.

Design, build, and maintain Finance data models and scalable data pipelines on GCP. Automate workflows (Python, Airflow/Astronomer, Jenkins), ensure data governance and lineage, collaborate with stakeholders, conduct code reviews and testing, and evaluate new data sources and technologies to support financial insights.

Build and operate scalable streaming and batch data pipelines for monetization, financial, and operational data. Develop canonical data models, reusable data products, data quality controls, observability, lineage, reconciliation, and auditability. Partner with Product Engineering, Finance, Accounting, Analytics, and GTM teams to define data contracts and deliver reliable platform capabilities. Lead technical designs, complex cross-functional projects, incident response, and engineering improvements.

Design, build, and optimize reliable data pipelines and Gold-layer data marts on Databricks Lakehouse in Azure. Develop batch and real-time streaming workflows, dimensional models, data contracts, automated quality tests, governance documentation, monitoring, and CI/CD processes. Collaborate with analytics and data science teams to resolve data issues, improve discoverability, and deliver trusted, high-performance data products.

Designs and optimizes ETL pipelines supporting analytics, data science, and machine learning. Builds labeling, retraining, MLOps, model monitoring, and production inference workflows. Contributes to lakehouse architecture, data modeling, governance, lineage, and quality. Uses infrastructure as code and containerization to create reproducible environments, collaborates with data scientists and BI teams, participates in code reviews, and improves engineering practices.

Design, build, and optimize enterprise-scale data pipelines and Adobe Experience Platform solutions (XDM, Real-Time Profiles, identity resolution) to enable segmentation, activation, governance, and AI-ready customer data. Collaborate with architects, product teams, and clients; ensure compliance and data quality; mentor junior engineers.

Design, build, test, and maintain cloud-based data pipelines using Azure, Databricks, Cosmos DB, Delta Lake, and Spark. Develop ETL frameworks, data quality validations, data warehousing solutions, and high-throughput libraries. Troubleshoot pipelines and DevOps deployments, analyze semi-structured data, and contribute across the software development lifecycle within an agile team. The role requires onsite work four days weekly and supports financial-industry products.

Design, build, and optimize enterprise-scale data pipelines and Adobe Experience Platform solutions. Implement XDM schemas, identity resolution, real-time profiles, and audience activation. Integrate AEP with Adobe marketing products, ensure governance and compliance, and mentor junior engineers while partnering with architects, data scientists, and stakeholders to deliver AI-ready customer data ecosystems.

Leads data engineering, cloud architecture, scientific software, and AI-enabled research platforms. Builds LLM, RAG, knowledge-search, and agentic workflows; integrates scientific systems; develops bioinformatics, genomics, clinical, and machine-learning data pipelines; and strengthens data quality, governance, lineage, security, observability, and reliability. Provides technical leadership across architecture, roadmaps, engineering practices, mentoring, and cross-functional collaboration.