Top Data & Analytics Jobs

Develop Python data pipelines and business data objects for applications. Build and deploy CI/CD pipelines, implement source-to-target mappings, resolve complex technical issues, and perform unit and integration testing. The role also involves software design, architecture, quality practices, cross-functional collaboration, and continuous improvement. The position is onsite five days per week in Chicago or Peoria, Illinois, with no relocation assistance or visa sponsorship.

Design, build, and maintain large-scale ETL pipelines and data warehouses (Redshift) with strong data modeling, governance, and quality controls. Optimize systems for performance and reliability, integrate and deploy AI/ML models, troubleshoot and support Redshift, collaborate with data scientists and stakeholders, and produce technical documentation and knowledge-base articles.

Build and architect scalable cloud data platforms, pipelines, and applications using technologies such as Python, Spark, Databricks, and Snowflake. Lead large-scale data initiatives, make architectural decisions, establish engineering patterns, and ensure platform resilience and operational efficiency. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical concepts to stakeholders. Evaluate data platform choices and deliver solutions supporting machine learning, distributed systems, and evolving enterprise data needs.

Lead large-scale data engineering initiatives, designing scalable cloud data platforms, pipelines, and architectures. Use Python, Spark, SQL, Databricks, Snowflake, AWS, relational and NoSQL databases to deliver resilient data solutions. Mentor engineers, analysts, and data scientists; establish engineering standards; evaluate platform choices; and collaborate with product and software teams to support enterprise data transformation and AI capabilities.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while applying distributed data, lakehouse, streaming, and full-stack technologies. Develop secure, resilient, and reusable data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Mentor technical contributors, enforce engineering patterns, communicate outcomes, and support data privacy and access controls.

Develop automated data-processing applications, algorithms, and machine-learning systems for F-15 flight-test engineering. Process, merge, visualize, and analyze aircraft and weapons-system data; perform troubleshooting and root-cause analysis; assess system performance against digital models; and collaborate with engineering teams on advanced radar, sensor, control, communications, and weapons technologies.

Leads the design, development, implementation, and support of scalable data solutions across multiple applications and platforms. Responsibilities include data requirements analysis, ETL/ELT development, data modeling, migration and transformation, workflow orchestration, performance optimization, data quality, pipeline monitoring, and production support. The role also provides technical leadership through coding standards, code reviews, mentorship, and engineering best practices.

Build reliable ETL infrastructure, scalable data pipelines, and trusted datasets serving product and business teams. Partner with data scientists and stakeholders to model data, monitor pipelines, define delivery SLAs, automate workflows, and enable self-service analytics and AI-assisted data exploration. The role requires strong SQL, cloud data-platform expertise, object-oriented programming, data modeling, and experience delivering production-scale data solutions.

Design, build, test, and support scalable cloud data pipelines and platforms using Python, SQL, Spark, databases, and cloud data technologies. Develop data models, distributed and streaming workloads, reusable data products, and end-to-end data solutions. Enforce engineering design patterns, security controls, encryption, and access management. Collaborate with product managers, engineers, analysts, and data scientists while mentoring developers and communicating technical outcomes to stakeholders.

Designs, builds, and operates scalable, resilient cloud data platforms and pipelines. Leads large-scale data initiatives, makes architectural decisions, evaluates technologies such as Snowflake and Databricks, and develops solutions using distributed data systems, relational and NoSQL databases, and cloud platforms. Collaborates with product and engineering teams, establishes reusable design patterns, communicates technical concepts, and mentors data engineers, analysts, and scientists.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Architect reusable engineering patterns, improve resilience and operational efficiency, enforce data security, and support real-time workloads. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical outcomes to stakeholders.

Design and implement cloud-based data lakes, databases, ontologies, ETL frameworks, and AI/ML data environments. Lead architecture for complex structured and unstructured data systems, optimize performance, troubleshoot production issues, collaborate with technical and research stakeholders, and mentor junior engineers. The role also involves DevSecOps, infrastructure automation, security design, technical leadership, and occasional travel while working onsite in McLean, Virginia.

Design, build, deploy, and support scalable cloud data solutions, pipelines, platforms, and applications for enterprise risk management. The role uses Python, Spark, SQL, relational and NoSQL databases, Databricks, Snowflake, and cloud technologies. Responsibilities include data modeling, distributed and streaming workloads, security controls, architecture standards, observability, operational efficiency, stakeholder communication, mentoring, and collaboration with product, engineering, analytics, and machine learning teams.

Design, build, and support scalable cloud data pipelines and platforms using Python, Spark, SQL, AWS, Databricks, Snowflake, and relational or NoSQL databases. Develop distributed, real-time, and batch data solutions; enforce reusable engineering patterns, security controls, scalability, and operational efficiency. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring peers and communicating technical outcomes to stakeholders.

Build and maintain scalable data pipelines, data applications, planning tools, APIs, and automated quality processes for network capacity planning. Develop ETL/ELT workflows, anomaly detection, monitoring, metadata and lineage solutions, and machine learning or AI-enabled applications. Analyze data, optimize performance, document systems, and collaborate with engineering, product, operations, software development, and data science teams to deliver reliable production solutions.

Designs and scales enterprise data platforms, pipelines, and integration frameworks for structured and unstructured data. Builds Snowflake and cloud-based AWS/GCP solutions, leads legacy-platform modernization and ETL/ELT implementation, and develops AI-ready datasets and data products. Establishes data governance, security, monitoring, and CI/CD practices for reliable, compliant platforms supporting analytics, machine learning, generative AI, and automation.

Designs reusable data ingestion and transformation components, automates data quality, governance, testing, lineage, and deployment workflows, and improves pipeline observability and reliability. The role supports monitoring, incident response, cloud and container integrations, infrastructure as code, documentation, reusable datasets, and Agile platform improvements. Candidates need five or more years of data engineering experience plus expertise in orchestration, distributed processing, storage, scripting, automation, and version control.

Implements data pipelines and platform features, automates validation and operational tasks, supports CI/CD and infrastructure-as-code updates, monitors platform reliability, assists with cloud configuration, upgrades, migrations, and performance tuning, and maintains technical documentation. The role also supports incident escalations and recommends incremental improvements. Requires data engineering or software/platform experience, data modeling and orchestration knowledge, scripting, version control, and a bachelor’s degree or equivalent experience.

Designs reusable data ingestion and transformation components, automates data quality, governance, lineage, and deployment checks, and improves pipeline reliability through monitoring and observability. The role supports cloud integrations, container orchestration, infrastructure as code, documentation, Agile planning, and incident response while partnering with data consumers to deliver usable datasets. Requires 5+ years of data engineering experience, distributed processing and storage expertise, scripting, automation, and version control skills.

Implements data pipelines and platform features, automates validations and operational tasks, supports CI/CD and infrastructure-as-code changes, monitors platform reliability, assists with migrations and performance tuning, and maintains technical documentation. The role also contributes to data quality, incident response, cloud configuration, and incremental platform improvements. This is a hybrid position with potential urgent IT incident support responsibilities.

Design, build, and optimize end-to-end data pipelines and architectures using Databricks, Spark, AWS, Airflow, and Kafka. Develop ETL processes, data models, warehouses, and reliable Python and SQL solutions while ensuring data quality, security, and governance. Collaborate with data scientists, analysts, and stakeholders to support analytics and AI/ML initiatives. Monitor workflows, troubleshoot issues, validate AI-assisted outputs, and improve performance and resiliency.

Build and maintain Terraform-managed Databricks data platforms on AWS, develop secure external data-sharing capabilities, implement data governance controls, automate audit evidence collection, and improve platform reliability, performance, monitoring, and cost efficiency. The role also involves incident resolution, on-call participation, data pipeline development, compliance collaboration, and designing secure-by-default infrastructure for publisher partners.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Build and maintain Databricks data pipelines and reusable models for Go product analytics. Define metrics, improve event logging, connect product usage with customer data, and support adoption and growth analysis. Ensure data quality through testing, monitoring, troubleshooting, and documentation. Optimize pipeline performance and costs while collaborating with Product, Engineering, and Data Science. Contribute dashboards and analyses that support product and business decisions.

Designs, builds, and operates scalable cloud data pipelines, platforms, and applications. Responsibilities include data modeling, distributed data processing, relational and NoSQL database development, architecture decisions, observability, streaming pipelines, and data warehousing. The role leads large-scale data initiatives, evaluates technologies such as Snowflake and Databricks, collaborates with product and engineering teams, communicates technical concepts, and mentors data engineers.