Top Data & Analytics Jobs

Design, build, and maintain ETL/data pipelines and data models (dbt). Work with large datasets across RDBMS and NoSQL, deploy to cloud (AWS preferred), use Python and orchestration tools (Dagster/Prefect/Airflow), support BI/analytics and visualization, and implement data reconciliation for accuracy.

The Data Engineer at Anteriad will design, build, and optimize data pipelines, manage data integration processes, support analytics efforts, and collaborate with stakeholders to deliver scalable data solutions.

Senior individual contributor who designs, builds, and evolves Azure-based enterprise cloud data platform capabilities. Guides architecture, standards, reusable components, automation, security, and enablement for analytics, ingestion, storage, processing, and operationalization while providing hands-on technical leadership and mentorship.

Lead architecture and evolution of Headway's data platform (warehouse, ingestion, orchestration, CI/CD, monitoring, cloud infra). Serve as technical anchor across analytics, product, and ML teams, drive platform roadmaps, set standards, mentor engineers, and own end-to-end infrastructure decisions for scale and performance.

As a Senior Lead Data Engineer, you'll lead a development team, design and implement data solutions using machine learning, distributed systems, and full-stack technologies, while collaborating with Agile teams and ensuring code quality.

Build and optimize high-volume data processing jobs and libraries (Redshift, Athena, Iceberg, Spark, Airflow). Debug and improve query and job performance, and collaborate with data scientists, analysts, and product managers to support analytics and data products.

Lead Data Engineer responsible for designing, building, testing, and supporting scalable data pipelines and cloud-based solutions. Collaborate across Agile teams to optimize data models, ensure data quality and governance, mentor engineers, and apply distributed computing, streaming, and emerging GenAI tooling to deliver personalized marketing and messaging experiences.

Lead Data Engineer responsible for designing, building, and supporting scalable cloud data pipelines and platforms. Collaborate across Agile teams to implement distributed computing, data modeling, quality, governance, and real-time streaming solutions using Python, SQL, Snowflake/Databricks, Spark, Kafka, and cloud services. Mentor engineers, participate in code reviews, and drive technical innovation including GenAI tooling.

Work at the intersection of software engineering and operations to investigate production issues, improve reliability, build automation, enhance observability, and support customers. Develop and maintain C# and SQL Server solutions, reduce manual effort, convert production learnings into lasting improvements, and provide occasional after-hours support.

Own end-to-end consumer data domains: ingest, clean, model, and productionize derived attributes and pipelines. Investigate new data sources, ensure quality and provenance, and partner with engineers, data scientists and product teams to deliver trustworthy data products that power applications, agents, APIs and ML models.

Build and mature data services and analytics in a cloud environment: gather requirements, design data models and pipelines, create Power BI reports, implement data quality checks, support statistical/NLP/AI modeling, and deliver data-driven insights to business stakeholders.

Build, maintain, and optimize cloud-based data pipelines and ETL processes. Collaborate with business stakeholders to design data models and deliver scalable warehousing solutions. Implement data quality checks, monitor performance, document systems, and enable self-service analytics (Power BI). Support migration from legacy systems and drive data engineering best practices across environments.

Maintain and monitor data pipelines that aggregate healthcare claims for the Cost Roll process. Validate and reconcile claim and provider demographic data, troubleshoot data issues, perform data quality checks, and collaborate with stakeholders to ensure reliable downstream financial and clinical systems.

Design and maintain ELT pipelines and Snowflake data models, build dbt transformations, integrate POS and telemetry sources, orchestrate pipelines on AWS, deliver Power BI reports, apply LLM/MCP tooling to automate workflows, and partner with stakeholders to produce production-grade data products and analytics.

Design, build, and maintain ELT pipelines and Snowflake/dbt models; integrate POS and telemetry sources; orchestrate pipelines on AWS; analyze data and produce Power BI dashboards; apply LLM/MCP tooling to automate workflows; collaborate with engineers and business partners to deliver production-grade data products.

Build, run, and maintain end-to-end data pipelines for mass spectrometry and molecular datasets. Ingest, convert, label, and enrich messy scientific data; implement data quality checks, monitoring, tests, and reproducible builds for ML training and product-read datasets.

Contribute to building and maintaining cloud-based data pipelines and data products. Develop, test, and optimize SQL and Python transformations, participate in code reviews, troubleshoot data quality issues, and follow CI/CD and documentation practices to support analytics and AI initiatives.

Design, build, and maintain automated ETL/ELT pipelines to integrate enterprise systems, ensure data quality, and deliver curated business-ready datasets. Develop Python-based data processing, document architectures and workflows, support Palantir and ERP platforms, and collaborate with stakeholders to define requirements and scalable solutions.

Build and maintain AEP data pipelines, CTAS queries, and XDM schemas to populate profile-enabled datasets. Monitor identity resolution and profile hygiene, manage CJA connections and data views, troubleshoot AEP dataflows, and document data governance and segmentation practices to support customer experience activation.

Build and operate streaming and batch data pipelines for energy-storage telemetry. Design reliable, query-ready time-series and event datasets using open table formats, orchestration frameworks, AWS, and infrastructure-as-code. Lead technical designs, partner with field operations and engineering teams, support on-call operations, and mentor engineers while improving data reliability and engineering practices.

Lead design and build scalable data processing and control-plane solutions on Databricks/AWS. Develop Python/PySpark/SQL pipelines, APIs/SDKs, governance, observability, CI/CD, and operational runbooks. Mentor engineers, drive adoption of AI-assisted development, and ensure data quality, security, lineage, and platform reliability.

Lead end-to-end design, development and deployment of data warehouse and reporting solutions for public/private loan, CLO and credit trading desks. Collaborate with portfolio management, operations and finance, manage offshore teams, integrate vendor products, build monitoring tools, and enhance dashboards and data access using Microsoft BI and data platform technologies.

Designs, builds, and maintains scalable data infrastructure, streaming and batch pipelines, and data products. Works with stakeholders to model, transform, and deploy data solutions using cloud, streaming, and ETL frameworks while ensuring governance, observability, and automated CI/CD deployments.

Develop interactive dashboards, visual analytics platforms, and automated reports for SATCOM, network operations, engineering, and operational data. Integrate machine learning model outputs into user-facing interfaces, including predictions, confidence intervals, explainability, and performance metrics. Establish visualization standards, guide technical architecture, collaborate across engineering and data science teams, mentor junior staff, and present findings to customers and senior leaders.

Design, develop, and maintain Omni-based dashboards, visualizations, and reporting solutions. Manage data products throughout their lifecycle, ensure data quality and standardization, recommend scalable analytics approaches, and translate business needs into trusted data solutions. The role also provides training, ongoing support, enhancements, and subject-matter expertise in data visualization and reporting best practices.