Top Data & Analytics Jobs

Architects and builds scalable data infrastructure, including AWS-based pipelines, data models, and secure data warehouses. Develops Airflow pipelines across S3 and Redshift, transforms data with dbt, integrates third-party and transactional data sources, optimizes queries, and implements data quality controls. Serves as a data architecture subject matter expert, establishes engineering best practices, troubleshoots complex failures, and translates stakeholder analytics needs into reliable technical solutions.

The Senior Data Engineer will design, implement, and manage data pipelines for real-time analytics and ML training datasets, ensuring efficient data flow and quality in a hybrid environment.

Architect and scale high-throughput infrastructure for billions of multimodal video clips, including storage, indexing, retrieval, observability, workload balancing, versioning, lineage, and dataset exploration tools. Optimize distributed compute and cloud systems for reliability, latency, throughput, and cost. Support VLM integration into data pipelines and collaborate with research and ML systems teams. Staff candidates independently define technical direction and architecture.

Lead development of a unified fleet knowledge model and decision-capture pipeline for autonomous data-center operations. Build data access layers across relational, telemetry, monitoring, and time-series systems; capture labeled operator decisions; negotiate data requirements with source-system owners; and own scaling considerations including vector indexing, schema evolution, retention, and analytics separation. The role requires strong infrastructure-domain modeling, PostgreSQL expertise, and experience handling time-series and event data at scale.

Own the strategy and architecture for a scalable, multi-cloud data platform across Revv and AirPro. Integrate Azure SQL Server data with AWS, Fivetran, dbt, and Snowflake pipelines; build trusted dbt models; connect Salesforce and ERP systems; and establish governance, quality, ownership, and compliance foundations. Partner with analytics, finance, sales, product, and engineering leadership to translate business needs into reliable data assets and customer-facing products.

Designs and leads scalable data pipelines, ETL processes, data models, analytics products, dashboards, and data quality frameworks. Partners with data scientists, product, security, and compliance teams to integrate production AI/ML solutions, establish governance, and support responsible AI practices. Provides technical leadership through architecture, design reviews, mentoring, roadmap alignment, and evaluation of emerging data technologies.

Build and operate a Kubernetes-based data platform spanning lakehouse storage, ingestion, transformation, entity resolution, lineage, and data quality. Develop batch and streaming pipelines across heterogeneous and disconnected environments, create full-stack tools for subject matter experts, and expose documented APIs for applications and AI agents. Collaborate with product, AI/ML, infrastructure, mission partners, and field teams while mentoring engineers and supporting secure, air-gapped deployments.

Design and operate scalable data pipelines, transformation workflows, layered data models, and production data infrastructure supporting analytics, experimentation, and AI/ML. Implement monitoring, data quality, observability, and incident response processes; modernize legacy systems; collaborate with product, analytics, and engineering teams; and mentor engineers through reviews and architectural discussions.

Own end-to-end production data platforms, writing Python and SQL, designing AWS pipelines, modeling analytics data, and implementing quality and reconciliation controls. Make architecture decisions independently, ensure correctness and auditability, and use AI coding tools effectively. The role also supports applied AI initiatives including entity resolution, LLM-assisted analytics, embeddings, and anomaly scoring.

Develop and maintain data transformation pipelines, data models, dashboards, visualizations, and self-service analytics tools. Clean, validate, and analyze complex datasets; translate business needs into technical solutions and actionable insights; document workflows; and communicate findings to technical and non-technical stakeholders. The role requires Kibana expertise, strong ETL and data pipeline knowledge, collaboration skills, an active TS/SCI clearance, and Security+ certification.

Owns the end-to-end data platform, including Snowflake infrastructure, ingestion pipelines, Airflow orchestration, dbt transformations, governed gold-layer models, and metric definitions. Ensures reliability, performance, testing, documentation, governance, and CI/CD standards. Builds data systems from scratch, partners with analytics stakeholders, and uses AI-assisted development tools to accelerate engineering work. Requires strong SQL, platform ownership, software engineering practices, and experience with Snowflake, Airflow, and dbt.

Own Tennr’s end-to-end data engineering function, building reliable pipelines from application and EHR systems into Snowflake. Lead healthcare data discovery and integration across multiple systems, establish Snowflake security and governance, develop data models, and implement monitoring and alerting. Explore AI-enabled methods for extracting insights from unstructured clinical referral documentation while collaborating with internal teams and healthcare stakeholders.

Develop and maintain Power Automate RPA workflows and enterprise integrations. Build Python automation, ETL processes, Azure and Snowflake data pipelines, data models, quality frameworks, and monitoring solutions. Create Power BI semantic models, reports, DAX measures, and security configurations. Support streaming, API, and application integrations across Azure, Snowflake, Databricks, Kafka, and related platforms.

Designs and owns Python backend services, scalable data pipelines, Spark processing workflows, data models, ETL/ELT systems, and data distribution patterns. Manages Terraform-based cloud infrastructure, ensures data quality and observability, leads technical workstreams, performs production root-cause analysis, and mentors junior engineers. Collaborates with product, data, and platform leaders to deliver reliable, scalable solutions.

Lead the technical direction and hands-on development of scalable data pipelines, data-serving platforms, and AI-enabled data frameworks. Design systems across ingestion, transformation, serving, and consumption; improve data quality, observability, reliability, security, query performance, and infrastructure efficiency. Lead cross-team initiatives, resolve complex production issues, mentor engineers, guide architectural decisions, and apply AI effectively throughout software development.

Design, build, and operate scalable Azure data pipelines and Databricks lakehouse platforms. Modernize SSIS workloads, implement Spark and Delta Lake solutions, and ensure production reliability, observability, data quality, and performance. Champion TDD, XP, CI/CD, AI-assisted development, and engineering standards while mentoring engineers. Support event-driven architectures and potentially MLOps, feature stores, and high-volume transportation data systems.

Build and support scalable big data pipelines, integrations, and Data Lakehouse architecture for marketing, analytics, reporting, and AI applications. Develop data models, monitor data quality, troubleshoot production issues, and provide post-deployment support. Collaborate with analytics, data science, AI, business, and engineering teams to improve data accessibility and define long-term platform architecture. The role requires strong Python and PySpark expertise, cloud and distributed-systems experience, and familiarity with orchestration, data governance, and CI/CD tools.

Designs, develops, tests, troubleshoots, and deploys cloud-based data and analytics solutions using AWS, infrastructure as code, and software development practices. The role involves data integration, automation, application and data model design, system integration, code reviews, testing, troubleshooting, and stakeholder collaboration. It also provides informal leadership through mentoring, design sessions, coding sessions, and communication of complex technical concepts.

Designs and manages enterprise Databricks data platforms, including workspaces, clusters, Unity Catalog, SQL warehouses, Terraform infrastructure, and secure data ingestion pipelines. Builds medallion architecture with Delta Lake, supports Genie dashboards and self-service analytics, implements governance and access controls, and maintains CI/CD, testing, documentation, and operational processes. Partners with technical and business stakeholders and supports MCP connectors and agentic data integrations.

Own TAP’s data layer end to end, including ETL/ELT pipelines, BigQuery and dbt models, data cleaning, entity matching, quality monitoring, documentation, dashboards, and automated reporting. Investigate data delivery issues, improve reliability, use AI-assisted development responsibly, and answer program and leadership questions. As the first data hire, help establish standards, roadmap, and processes while collaborating across the organization.

Investigate difficult public and municipal data sources, reconstruct and validate datasets, design data models, and build production systems for acquiring and refreshing data. The role partners with operations and state expansion teams, prototypes solutions, reviews TypeScript, uses PostgreSQL and spatial data, and owns reliable production implementations supporting affordable housing site selection.

Build and optimize data transformation pipelines using SQL, dbt, Airflow, Python, BigQuery, and relational databases. Collaborate with stakeholders and data engineers, manage task scheduling, resolve data issues, perform analyses, present recommendations, support data exploration tool adoption, and mentor team members. The role also contributes to hiring, onboarding, and professional development while ensuring data accuracy and insight quality.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, improving data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable 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, ambiguous 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.