Top Data & Analytics Jobs

Designs and optimizes scalable batch and real-time data pipelines using Python, PySpark, Databricks, Snowflake, and Hadoop. The role supports legacy-to-cloud migration, performance tuning, data validation, governance, CI/CD, and automated testing. It partners with analysts, data scientists, and stakeholders to develop reliable analytical data products, diagnose data issues, and document technical solutions. AI coding assistants are used to improve development productivity and code quality.

Own Step’s analytics data models, transformation pipelines, metric definitions, semantic layer, and data quality practices. Partner with Product, Marketing, Engineering, Risk, Growth, and Finance to establish trustworthy data foundations and enable self-service analysis. Build reusable query, visualization, and code components; apply AI-augmented workflows; and collaborate with data scientists, analysts, and platform engineers. The role requires strong SQL, modeling, pipeline, BI, communication, and independent ownership skills, with occasional travel for team onsites.

Lead design and implementation of data infrastructure, pipelines, and warehouses using Azure Data Factory, Databricks, and Snowflake. Manage data modeling, quality, security, and analytics frameworks. Lead client accounts, mentor teams, and drive innovation to deliver data-driven business insights.

Design and build data infrastructure, pipelines, and transformations using Azure Data Factory, Azure Databricks, and Snowflake. Develop data architecture and modeling, maintain data lakes/warehouses, ensure data quality and security, optimize performance, and collaborate with clients to deliver tailored data solutions and governance.

Develops and implements data-driven solutions for LM Space Systems and the 1LMX transformation. Responsibilities include building and optimizing ETL, SQL, replication processes, data pipelines, models, reports, and visualizations across structured and unstructured data. Partners with architecture, DevOps, application, governance, and business teams to translate requirements into secure, governed, and scalable data solutions.

Leads the design, development, governance, and productization of enterprise data models, pipelines, data products, and ontologies. Builds machine-learning-ready datasets, implements security and lineage controls, optimizes Databricks and Delta Lake environments, establishes data-product standards, collaborates with architects and data scientists, and mentors engineering talent. The role also defines product roadmaps, service levels, usage metrics, and continuous-improvement processes for enterprise data assets supporting AI agents, analytics, and applications.

Processes and troubleshoots IRIG 106 flight-test data, coordinates with instrumentation, operations, IT, and leadership, and resolves data-recovery issues. Supports IT assignments, identifies processing needs and cost-saving solutions, and receives training in data processing, server administration, and desktop support. Requires strong analytical, troubleshooting, communication, and independent-work skills, with occasional extended hours and weekends.

Lead the architecture, development, and operation of scalable data platforms. Design ETL workflows, pipelines, data models, APIs, and distributed systems using Spark, Python, and SQL. Drive production deployments, infrastructure automation, observability, security, disaster recovery, and incident resolution. Make cross-team architecture decisions, apply AI-assisted engineering responsibly, improve testing and operational readiness, and mentor engineers while maintaining high standards for scalability, reliability, privacy, and cost.

Lead the architecture and development of UKG’s Unified FinOps Data Platform. Build scalable cloud-native data products and pipelines for cloud billing, usage, cost allocation, forecasting, optimization, reporting, and unit economics across AWS and GCP. Establish governance, data quality, lineage, observability, CI/CD, and infrastructure-as-code standards. Support AI-enabled forecasting and optimization, collaborate with Finance and Engineering stakeholders, mentor engineers, and drive adoption of self-service reporting.

Develop and maintain scalable big data applications, ETL/ELT pipelines, and real-time streaming solutions. Use Spark, Hadoop, Kafka, SQL, cloud platforms, and databases to integrate, transform, and process structured and unstructured data. Optimize performance, scalability, reliability, and data quality while implementing monitoring and security controls. Collaborate with engineers, scientists, and analysts, troubleshoot processing issues, and document technical solutions.

Build and manage data engineering solutions involving data modeling, ETL/ELT, integration, master data management, data quality, warehousing, analytics, business intelligence, and governance. Use Microsoft Azure, Informatica, Snowflake, and programming languages including Python, C#, SQL, and Java. Communicate complex technical concepts clearly, analyze data to identify opportunities, advise stakeholders, and drive strategic improvements.

Build and maintain data pipelines to ingest, normalize, and deliver messy healthcare data (FHIR, HL7, CCDAs, PDFs) into structured models for AI and clinical use. Implement ETL, real-time webhooks, document extraction, PII sanitization, storage on AWS, and monitor data quality while ensuring compliance with healthcare interoperability standards.

Build and maintain Snowflake-based enterprise data solutions, including scalable data pipelines, ELT workflows, data models, and integrations. Develop Snowflake objects and optimize query performance, resource utilization, and warehouse configurations. Use Python for data cleaning and integration, monitor data quality and pipeline completion, translate business requirements into technical specifications, and provide on-call support.

Designs, develops, and maintains Databricks-based ETL pipelines, data integration solutions, data warehouses, and reporting datasets. Builds batch and streaming architectures using Python, Spark, SQL, and Delta Lake; optimizes performance and costs; migrates legacy SSIS and SQL Server processes; maintains SSRS reports; implements data quality controls; and collaborates with business and technical teams supporting analytics and AI/ML workloads.

Design, build, and operate end-to-end data pipelines and models for healthcare claims and payer data. Responsibilities include ingestion, canonical and analytical data modeling, legacy process migration, data quality, testing, CI/CD, deployment, operations, incident response, tool integration, documentation, and onboarding new data sources. The role partners across ML, product, analytics, platform, operations, and customer teams while critically evaluating AI-generated engineering work.

Develop and maintain ServiceNow Performance Analytics and Power BI dashboards for enterprise ITSM reporting. Analyze incidents, requests, SLAs, customer satisfaction, and service performance data; build data models, DAX calculations, Power Query transformations, SQL/API integrations, and automated reports. Collaborate with technical teams and government stakeholders, support Agile activities, maintain data security and accuracy, and identify service trends and improvement opportunities in a DoD environment.

Build and operate scalable batch and streaming data pipelines, dimensional models, ELT/ETL workflows, and trusted data products. Improve data quality, observability, reliability, performance, security, and cost efficiency across the cloud data platform. Partner with analytics, risk, finance, product, data science, and machine learning teams to support business and AI use cases. Lead technical initiatives, influence architecture, mentor engineers, and establish engineering best practices.

Own Mux’s end-to-end data platform, including ingestion, Snowflake administration, dbt transformations, orchestration, data quality, observability, and reverse ETL. Build semantic models supporting AI agents, establish governance and reliability standards, and partner with Engineering, Finance, Product, and GTM. Use AI coding tools to develop and maintain pipelines while validating output quality. The role requires strong SQL, Python, data modeling, cloud warehouse administration, and production data engineering experience.

Leads quality data engineering and analytics initiatives, including data pipelines, integrations, databases, dashboards, governance, automation, and AI-enabled solutions. Translates business requirements into scalable systems, analyzes quality trends, improves processes, administers quality platforms, and partners with IT and vendors. Provides technical leadership, training, mentoring, and support while driving adoption of Power Platform, intelligent agents, copilots, and responsible AI practices.

Build, test, deploy, and operate data pipelines and transformations for financial and economic datasets. Monitor production workflows, implement alerting, logging, automated testing, and data-quality controls, and perform root-cause analysis. Collaborate with engineering, DevOps, application, investment, and research teams to deliver documented solutions. Analyze investment data, support model research, and identify automation opportunities while developing expertise in private markets data.

Develop accurate, scalable data visualizations, dashboards, data models, and documentation using clinical and enriched healthcare data. Perform custom analyses for account managers and clinical partners, support data investigations, and collaborate with product, engineering, clinical, and account teams. Ensure data product quality and timeliness while building solutions with SQL, relational data, Looker, and business intelligence tools.

Leads development and optimization of very large SQL Server databases, including performance tuning, data modeling, architecture, ETL pipelines, security, and access management. Oversees SQL code reviews, stored procedure optimization, database encryption, and collaboration with DBA teams. Mentors developers and analysts, guides project delivery, assesses risk, and uses AI tools to improve SQL development and productivity.

Designs, develops, optimizes, and supports scalable ETL pipelines and large-scale data platforms using Python, PySpark, SQL Server, SSIS, and Hadoop technologies. Responsibilities include database performance tuning, data modeling, production troubleshooting, scheduling automation, data-quality controls, CI/CD, secure development, and collaboration across technology and business teams. The role also mentors developers and evaluates AI-assisted development tools.

Designs, builds, deploys, and operates scalable data pipelines, data lakes, warehouses, and cloud architectures supporting lending analytics. Responsibilities include schema design, SQL optimization, workflow orchestration, monitoring, troubleshooting, data quality management, and applying generative AI to engineering tasks. The role also contributes to data engineering standards and collaborates with analysts, engineers, and governance teams to deliver reliable production data systems.

Develop and maintain Python-based data analytics applications, pipelines, integrations, dashboards, and data quality solutions for Prime Services financial data. Architect scalable systems using Kafka, Spark, S3, Trino/Starburst, and databases. Translate requirements from business stakeholders and quantitative analysts into production software, support global batch operations, troubleshoot incidents, and improve monitoring through AI-powered agents. Ensure data accuracy, lineage, regulatory compliance, and operational resilience across distributed financial technology teams.