Top Data & Analytics Jobs

Supports data and software development initiatives through code debugging, performance improvements, troubleshooting, code reviews, documentation, and technical problem-solving. Collaborates with team members to develop reliable data systems, learn emerging technologies, and communicate project updates. The intern also evaluates and applies AI tools responsibly to improve workflows and insights.

Designs, integrates, and manages scalable batch and streaming data pipelines from internal and external sources. Responsibilities include data transformation, schema and business-rule enhancements, load monitoring, troubleshooting data availability, evaluating emerging technologies, and collaborating with business, engineering, and privacy teams. The role also supports data governance, secure data handling, cloud technologies, and adoption of AI-enabled tools.

Designs, integrates, and manages scalable batch and streaming data pipelines from internal and external sources. Builds ETL processes, data schemas, business rules, and cloud-based data solutions; monitors data loads and resolves availability issues. Collaborates with business, engineering, and privacy teams to ensure compliant data handling. Evaluates emerging data technologies, supports innovation, and helps adopt modern architectures, DevOps practices, and AI-enabled workflows.

Designs and optimizes data architectures, pipelines, databases, and Big Data solutions. The role develops partitioning and indexing strategies, supports scalable and secure data infrastructure, documents enterprise data standards, and collaborates with architects, engineers, product teams, and business stakeholders. Responsibilities include database performance tuning, distributed systems development, data modeling, and alignment with Agile delivery practices.

Design, develop, test, and implement data engineering features using Python and PySpark. Build standalone scripts, perform complex data transformations, translate business requirements into technical solutions, troubleshoot data and software issues, and enhance existing frameworks. Apply object-oriented programming, coding best practices, automated testing, documentation, and large-scale data processing while collaborating with team members and working independently.

Designs and maintains scalable data infrastructure, including ETL/ELT pipelines, data lakes, warehouses, validation processes, and governance controls. Collaborates with architects and business stakeholders to translate requirements into reliable data solutions. Uses Docker, Kubernetes, cloud platforms, Spark, and orchestration tools to deploy and automate data services. Supports AI and automation initiatives involving LLMs and agents while improving data quality, security, and accessibility.

Build and optimize scalable data pipelines and models for pharmacy, claims, pricing, reimbursement, MTM, and rebate data. Responsibilities include data ingestion, cleansing, transformation, validation, governance, quality monitoring, root-cause analysis, testing, code reviews, performance tuning, and cloud data architecture. The role collaborates with analysts, data scientists, engineers, and healthcare stakeholders to deliver reliable data assets, support analytics and machine learning, and improve DataOps practices.

Leads production data engineering for AI systems, designing scalable pipelines, cloud infrastructure, orchestration, retrieval architectures, evaluation datasets, and real-time data flows. Establishes data quality, governance, lifecycle, observability, and coding standards; integrates AI agents into engineering workflows; manages significant incidents; builds reusable platform components; and mentors junior and mid-level engineers.

Develop and maintain scalable data pipelines, models, datasets, reports, and dashboards for operational reporting. Use dbt, Airflow, Azure Data Factory, SQL, Power BI, and related technologies to build, optimize, test, migrate, and support data delivery solutions. Collaborate with technical and business stakeholders, conduct root cause analysis, automate maintenance, contribute to development standards, and mentor junior engineers. The role requires P&C insurance experience and hybrid work three days per week at a specified office location.

Leads the strategy, architecture, and delivery of complex data platforms supporting investment research and portfolio construction. Designs Databricks and AWS lakehouse pipelines, data models, governance, testing, observability, and AI-ready metadata. Guides AI-agent engineering workflows, ensures security and reliability, manages cross-team initiatives, and leads technical reviews and engineer development. Partners with investment professionals and product managers to deliver trusted data products and scalable AI capabilities.

Leads the design, development, and maintenance of scalable data pipelines, enterprise relational databases, and secure data integrations. Transforms structured and semi-structured data for analytics and visual intelligence applications, improves data quality, establishes database and security standards, and supports high-performance distributed data processing. The role requires extensive experience with SQL, relational databases, enterprise data warehouses, cloud infrastructure, ETL technologies, and secure data practices in a highly classified, largely onsite environment.

The Data Engineer will design and implement ETL frameworks, perform data analysis, troubleshoot data issues, and optimize data models to meet business needs.

Design, build, deploy, and optimize enterprise Lakehouse data pipelines and ETL processes using Databricks, AWS, Python, and PySpark. Responsibilities include data modeling, Delta Lake and Unity Catalog implementation, performance tuning, pipeline troubleshooting, data security and compliance, legacy SQL analysis, and translating raw data into actionable insights. The role also involves cross-functional collaboration and may require eligibility for a Public Trust or Suitability/Fitness determination.

Designs, implements, and maintains secure, scalable ETL pipelines, data warehouses, and Azure data environments. Responsibilities include developing SSIS and Azure Data Factory solutions, creating Azure DevOps CI/CD pipelines, testing and debugging code, documenting procedures, improving data infrastructure, managing projects, and supporting SharePoint sites. The role requires collaboration with stakeholders, strong SQL and database design skills, and independent problem-solving.

Develop and maintain reliable data pipelines, transformation workflows, PostgreSQL solutions, APIs, and reporting integrations using Python, SQL, Databricks, and AWS QuickSight. The role includes data quality monitoring, testing, troubleshooting, security controls, documentation, CI/CD collaboration, and Agile participation. Candidates will translate technical and business requirements into maintainable code while collaborating with data scientists, analysts, DevOps engineers, and senior technical staff.

Build and maintain reliable data pipelines supporting money movement, fraud response, and client reporting. Integrate internal and external APIs, orchestrate workflows with Airflow on Kubernetes, and implement testing, monitoring, alerting, and observability. Debug production issues, document root causes, improve pipeline efficiency, collaborate with stakeholders, and contribute to technical designs. The role requires Python, SQL, Airflow, Snowflake, dbt, and GCP experience, with opportunities for increased end-to-end ownership.

Leads end-to-end data engineering solutions, including pipeline design, data modeling, testing, deployment, optimization, production support, and data quality. Partners with stakeholders to develop cloud-based data platforms, prototypes, technical standards, and scalable analytic systems. Provides technical leadership, conducts design and code reviews, mentors team members, and ensures compliance with risk and data management policies.

Build, maintain, and optimize data warehouses and ETL/ELT pipelines supporting ticketing, sales, marketing, fan engagement, and analytics. Integrate NFL and third-party data into Snowflake and Azure, develop modeled datasets, monitor data quality and ingestion, troubleshoot pipeline issues, and document schemas and workflows. The role also supports privacy, consent, data governance, and cybersecurity controls while collaborating with technical and business stakeholders. This is a full-time, onsite position in Indianapolis.

Participates in a 10-week data engineering internship supporting data profiling, reporting, data generation, relational data modeling, data management, documentation, and development of data pipelines and models. The role involves understanding data architecture, accessing and manipulating data, and applying analytical skills to produce reports, datasets, and self-service outputs.

Designs, develops, deploys, and supports scalable batch and streaming data pipelines using Databricks and lakehouse technologies. Builds governed data products for analytics, AI, machine learning, and enterprise insights. Responsibilities include data ingestion and transformation, orchestration, quality validation, observability, governance, performance optimization, troubleshooting, testing, documentation, and production support. Collaborates with engineers, architects, product owners, and business stakeholders across the full engineering lifecycle.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Design, build, and maintain large-scale GCP data pipelines and data warehouse solutions using Dataproc, GCS, BigQuery, Hadoop/Spark/Hive and Airflow. Mentor engineers, run agile ceremonies, prioritize backlog, provide on-call support, and deliver data models and products at multi-TB/PB scale.

Leads the design and development of cloud-native data and business intelligence platforms using Databricks, Snowflake, AWS, and Azure. Builds scalable pipelines, analytics frameworks, CI/CD automation, infrastructure-as-code, and secure multi-cloud integrations. Optimizes performance, reliability, governance, and cost while leading architecture decisions, technical reviews, cross-functional initiatives, and engineer mentorship.

Build and maintain Nexus, an enterprise AI implementation platform. Develop backend services, APIs, integrations, orchestration workflows, automated data pipelines, transformations, semantic models, validation processes, and governed lakehouse assets. Partner with AI engineers and enterprise customers to deploy solutions, resolve complex data and system requirements, and create reusable integrations. Ensure secure, monitored, recoverable, and repeatable deployments across customer environments.

Design, implement, and optimize large-scale data processing on Databricks and Spark. Build ETL pipelines, integrate data across AWS services (Redshift, RDS), orchestrate workflows with Airflow, and ensure data warehousing and engineering best practices. Lead and mentor development teams and collaborate with cross-functional stakeholders to drive data strategy and execution.