Top Data & Analytics Jobs

Build and operate large-scale data pipelines that acquire, process, and improve audio datasets for AI model training. The role focuses on data quality, throughput, cost optimization, production systems, and collaboration with AI scientists. Responsibilities include discovering data sources, developing reliable processing systems, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data-processing systems, optimizing data quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement in a distributed, autonomous environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data acquisition and processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement, with emphasis on backend development, production systems, and scalable data processing.

Build and operate large-scale data pipelines that collect, process, and optimize audio datasets for AI model training. The role involves discovering data sources, improving data quality, throughput, and cost efficiency, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Designs and maintains secure, reliable data platforms, including relational data models, migrations, pipelines, monitoring, data quality processes, and self-service reporting access. The role protects PHI and PII through access controls, encryption, audit logging, and governance in a HIPAA environment. It also involves backend API development, code reviews, security reviews, stakeholder collaboration, and mentoring teams on data practices.

Manage and evolve a cloud-native enterprise data platform across Azure, Databricks, Unity Catalog, Collibra, and Immuta. Optimize platform performance, security, scalability, observability, access, workloads, and cost. Design and maintain lakehouse architectures, batch and streaming pipelines, integrations, governance controls, and data-quality processes. Automate infrastructure and deployments using infrastructure as code and CI/CD. Lead incident response, upgrades, resilience activities, technical documentation, design reviews, and coaching while collaborating with engineering, security, architecture, governance, and business teams.

Leads the design and delivery of scalable infrastructure data systems, including ingestion pipelines, transformations, data models, and services for usage, cost, ownership, and planning. Establishes technical direction, data contracts, quality controls, monitoring, and operational processes while leading cross-functional initiatives and mentoring engineers. Uses AI responsibly to improve development and data-quality workflows while validating outputs and protecting sensitive information.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Design and optimize large-scale Databricks Lakehouse data pipelines in Azure. Modernize cloud data ecosystems, develop Python, PySpark, Spark SQL, and ETL/ELT solutions, and implement Delta Lake, Unity Catalog, governance, lineage, security, and CI/CD automation. Build ingestion frameworks for APIs, databases, files, streaming sources, and MDM systems. Support BI, data science, and application workloads while leading cross-functional data initiatives involving regulated financial datasets.

Design and optimize large-scale data pipelines on Databricks and Azure. Modernize cloud data ecosystems, develop ETL/ELT solutions with Python, PySpark, and SQL, and implement Delta Lake, Unity Catalog, governance, lineage, security, and CI/CD automation. Build ingestion frameworks for APIs, databases, files, streaming, and MDM systems. Support BI, data science, and application workloads while leading cross-functional data initiatives involving regulated financial datasets.

Develop Power BI reports, dashboards, dimensional data models, and optimized reporting solutions. Extract and transform data using SQL, DAX, and M, while gathering business requirements and supporting data collection processes. The role also requires C#/.NET microservice development, SQL Server engineering, Docker and Kubernetes familiarity, GitHub Actions CI/CD, production troubleshooting, and application support. Experience with Power Platform, React, APIs, automated testing, cloud platforms, and construction or semiconductor construction is valuable.

Develops analytics solutions for learning and assessment data, including ETL scripts, data models, machine learning models, statistical analysis, and Power BI dashboards. Partners with faculty to gather requirements, generate insights, analyze student learning behavior, and deliver descriptive and diagnostic analytics. Provides dashboard training, consultancy, and knowledge transfer to staff while applying generative AI and large language model techniques.

Build and maintain high-performance data lakehouse infrastructure, including Apache Iceberg maintenance workflows, compaction, snapshot expiry, manifest rewriting, and orphan file cleanup. Develop Rust-based SQL routing components, connectors, and query infrastructure. Ensure data integrity under concurrency and failures, contribute to open-source projects, operate production systems, participate in on-call support, and debug customer environments.

Develop and manage enterprise data pipelines and dbt transformations in Snowflake, from source ingestion through data marts. Responsibilities include data integration, modeling, testing, observability, security, performance tuning, troubleshooting, documentation, metadata management, and architecture. The role collaborates with technical and business stakeholders to deliver reliable, governed, high-quality data products and continuously improve the data platform.

Leads the design and implementation of scalable data engineering solutions for Corporate Services and HR. Translates business needs into production-ready pipelines, enterprise integrations, automation, and AI-ready data architectures. Builds rapid prototypes, improves workflows, mentors technical teams, manages stakeholder relationships, and delivers measurable efficiency gains. Responsibilities include data quality, governance, observability, orchestration, cloud platforms, generative AI enablement, and ongoing production support.

Design, build, optimize, and maintain scalable data pipelines, APIs, and microservices for high-volume regulatory datasets. Ensure data quality through automated testing, monitoring, and validation; contribute to platform architecture; integrate with NLP/ML services and ELK; support production deployments and incidents; and collaborate with Engineering and Product teams. Mentor engineers, participate in code reviews, and provide technical guidance.

Build scalable Databricks Lakehouse architectures and ETL/ELT pipelines using Spark and Delta Lake across AWS, Azure, and GCP. Tune performance and cloud costs, integrate MLflow for AI model deployment, and implement CI/CD and Infrastructure-as-Code with Terraform or Databricks CLI. The role also enforces governance and security through Unity Catalog, supports regulatory compliance, and collaborates with clients, data scientists, architects, and engineering teams to deliver production-ready AI solutions.

Leads the architecture and evolution of scalable data platforms, pipelines, and data products across multiple teams. Designs Snowflake and cloud-based solutions for batch, streaming, CDC, ingestion, transformation, orchestration, and data delivery. Establishes engineering standards, governance, observability, testing, CI/CD, and reliability practices; mentors engineers; guides architecture and roadmap decisions; resolves complex incidents; and evaluates AI, automation, and MCP-enabled data capabilities.

Design and build cloud-agnostic Databricks Lakehouse architectures, scalable ETL/ELT pipelines, and production AI data workflows. Optimize Apache Spark performance, integrate MLflow for model deployment, and implement CI/CD and Infrastructure-as-Code using Terraform or Databricks CLI. Enforce governance, security, and compliance standards through Unity Catalog and cloud platforms. Collaborate with architects, scientists, product managers, and client engineering teams to deliver documented, production-ready solutions.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Design and implement lakehouse architectures, data pipelines, semantic models, and enterprise Power BI dashboards using Microsoft Fabric and Databricks. Optimize SQL, Spark, and DAX workloads while ensuring data quality, scalability, security, and maintainability. Consult with enterprise clients, translate business needs into technical solutions, provide platform guidance and training, manage stakeholders, and contribute to internal engineering standards and reusable practices.

Develop and maintain cloud-native ETL pipelines and modernize the firm’s data architecture using Azure, Databricks, and Azure Data Factory. Build governed, reliable data products supporting reporting, analytics, automation, and AI use cases. Collaborate with technical and business stakeholders on data quality, security, lineage, access controls, testing, DevOps, and infrastructure-as-code practices.

Build and operate production-grade ELT pipelines from Benchling, Salesforce, and third-party systems into Snowflake using dbt. Own ingestion, transformation, warehouse, BI, governance, monitoring, testing, access controls, privacy, cost, and performance. Partner with AI engineering to provide trusted data for internal AI applications and agentic workflows, contribute to warehouse and metrics-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.

Designs, develops, and maintains scalable data pipelines, ETL/ELT workflows, and data platforms for Intelligence Community missions. Transforms structured and unstructured data using Python, SQL, Scala, or Java; supports ingestion, processing, monitoring, reliability, and data quality. Collaborates with analysts, developers, and data consumers in Agile teams, troubleshoots complex data environments, and translates mission requirements into technical solutions. The role is on-site in Chantilly, Virginia and requires an active TS/SCI clearance with polygraph.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.