Top Data & Analytics Jobs

Build, maintain, and optimize complex batch and streaming data pipelines across structured and unstructured sources. Partner with Engineering and Analytics teams to develop data exchange systems, translate business needs into technical solutions, ensure data and code quality, automate processes, support development environments, and participate in weekly on-call rotations.

Designs, builds, optimizes, and supports scalable AWS-based data infrastructure, pipelines, data models, and ETL/ELT processes using Python, SQL, PySpark, Databricks, and Spark. The role supports analytics, reporting, and business intelligence through production monitoring, troubleshooting, testing, CI/CD, documentation, and cross-functional collaboration with BI, Product, Engineering, Data, and client-facing teams.

Build and maintain data pipelines, ELT processes, and Data Vault-based warehouse solutions using modern cloud technologies. Translate stakeholder requirements into scalable data assets, improve data quality and performance, support production troubleshooting, and participate in testing, code reviews, documentation, and technical design. The role also uses AI-assisted development tools and collaborates with engineers, analysts, and business stakeholders.

Lead migration of on-premises databases and ETL workloads to Azure and Snowflake. Design scalable data platforms using Azure Data Factory, Databricks, ADLS, Logic Apps, Blob Storage, and Python. Modernize SSIS, T-SQL, and legacy ETL solutions while developing AI-powered capabilities using LLMs, semantic search, vector databases, document intelligence, and RAG architectures. Work independently, collaborate effectively, adopt new technologies, and travel up to 10%.

Build and govern Ibotta’s foundational data platform using Python, SQL, Terraform, Databricks, AWS, and Spark. Develop infrastructure provisioning, access controls, governance automation, self-service tooling, and scalable data pipelines. Support reliability through incident resolution, root cause analysis, on-call participation, and platform improvements. The role requires three years of software or data engineering experience and is hybrid in Denver.

Build and maintain Databricks data pipelines using SparkSQL and PySpark; ingest, transform, model, and validate healthcare data from payer, provider, platform, and API sources. Support automated file exchanges, dashboards, downstream workflows, testing, governance, access controls, and audit logging. Participate in design and code reviews, use AI coding tools responsibly, and progressively own data engineering work while handling protected health information.

Build and maintain Databricks data pipelines using SparkSQL and PySpark; ingest, transform, model, and validate data from payer, provider, claims, platform, and API sources. Support automated file exchanges, dashboards, testing, data governance, access controls, and audit logging. Use AI coding agents responsibly, participate in design and code reviews, document technical decisions, and collaborate with business teams. The role is based in Miami and targets candidates graduating between December 2026 and June 2027.

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.

Lead design and implementation of scalable data infrastructure, pipelines, and transformations using cloud platforms. Build and optimize data warehouses (Snowflake, Databricks), orchestrate workflows (Airflow, Hadoop), ensure data quality and security, and coach teams to deliver analytics solutions that drive tax practice insights and client outcomes.

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.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Leads data engineering teams and develops cloud-based, distributed, and full-stack data solutions. Responsibilities include designing, coding, testing, reviewing, and supporting systems using big data technologies, cloud data warehouses, streaming applications, machine learning systems, microservices, and NoSQL databases. The role also involves mentoring engineers, collaborating with product managers and Agile teams, optimizing performance, and driving technology transformation.

The Data Engineer will design data models, build data warehouses and lakes, automate data pipelines, and enable data accessibility while maintaining security standards in a complex environment.

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, interpret design requirements, and perform unit and integration testing. The role also promotes software quality practices, continuous improvement, and collaboration across functional teams. It requires onsite work five days per week in Chicago or Peoria, Illinois.

Develop and modernize scalable batch and real-time data pipelines and feature engineering solutions on Databricks. Build feature store capabilities, migrate legacy Spark and EMR workloads, implement cloud-native AWS solutions, and apply testing, CI/CD, observability, governance, security, and data-quality controls. Collaborate with data scientists and stakeholders, contribute to architecture decisions, support production systems, and mentor junior engineers.

Designs, develops, and maintains scalable data pipelines, transformations, datasets, and curated data products for credit risk, reporting, analytics, decisioning, and machine learning. The role uses SQL, Python, Databricks, and Spark; implements data quality controls; supports production monitoring and troubleshooting; collaborates with analytics, data science, product, and engineering teams; and contributes to cloud migration and data platform modernization.

Design, build, and operate large-scale ETL pipelines, data lakes, and data platforms processing billions of daily events. Own data quality, freshness, reliability, monitoring, and observability for foundational datasets. Develop self-service ETL frameworks and tooling, contribute to architecture and technical strategy, and partner with product, engineering, machine learning, data science, and leadership teams to deliver scalable data solutions.

Build and maintain data integration workflows and ETL/ELT pipelines using Google Cloud Data Fusion. Transform usage data, write SQL and data-processing scripts, monitor workflows, troubleshoot failures, profile data, and document architectures. The role also supports AI-powered data solutions, responsible AI practices, pipeline optimization, and continuous process improvement while collaborating with technical teams in a remote environment.

Build and operate scalable data pipelines and trustworthy data models supporting Layla’s AI-powered travel products. Own data quality, reliability, observability, experimentation, and model-evaluation foundations. Enable production AI/ML systems through feature and data availability, evaluation datasets, and monitoring. Partner with Product, Analytics, and ML teams to deliver dependable insights, establish engineering best practices, and mentor other engineers.

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Supports data infrastructure development, monitoring, optimization, and reliability. Responsibilities include managing relational databases, assisting with BI and Data Science pipelines, supporting Snowflake, Fivetran, and dbt warehouse systems, tuning queries and databases, establishing monitoring and alerting, working with AWS, and writing automated data model tests.

Build and maintain data pipelines and scalable cloud infrastructure supporting analytics and machine learning. Integrate internal and external data sources, ensure data integrity, contribute to analytical investigations, document findings, and apply data governance, security, and performance optimization practices while collaborating with senior engineers and cross-functional teams.

Build and optimize scalable batch and streaming data pipelines for Comcast’s enterprise Data Lake. Develop cloud-native solutions using AWS, Databricks, PySpark, Kafka, Kubernetes, and Airflow/MWAA. Support high-volume distributed data processing, production troubleshooting, data warehousing, automation, observability, and data quality initiatives. Collaborate with product, governance, analytics, and engineering teams, participate in architecture decisions, mentor junior engineers, and improve platform reliability and performance.

Supports the Mobile Device Data and Operations team by collecting, cleansing, enriching, and distributing product data to catalogs and vendor partners. Develops workflow designs and toolsets using KNIME or Alteryx, Oracle and Snowflake databases, and AWS. Participates in an 11-week full-time internship with professional development, networking, mentorship, and cross-functional activities.