Top Data & Analytics Jobs

Develop and maintain data pipelines, workflows, and applications that organize structured and unstructured data for reporting and analytics. Build scalable ETL/ELT solutions, retrieve and process data, and collaborate with analysts, developers, engineers, and data consumers in an agile environment. The role also involves assessing, designing, building, and maintaining analytic solutions for mission-driven clients.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop data warehouse models, transformations, automation, quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop warehouse models, transformations, automation, data quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Build and operate laboratory data infrastructure supporting Benchling, sample tracking, APIs, web applications, instrument integrations, and analysis-ready data. Develop integrations across automation platforms, instruments, LIMS/ELNs, imaging systems, databases, and downstream pipelines. Create scientist-facing tools, investigate data discrepancies, and apply robust software engineering practices. Partner across scientific, automation, IT, and data teams while independently owning laboratory systems and planning for future platform needs.

Designs and maintains a scalable data platform supporting analytics, machine learning, and AI workloads. Builds tooling for reliable data pipelines, semantic layers, data agents, LLM applications, and evaluation systems. Ensures data quality, availability, observability, and reliability while establishing modeling, testing, and platform best practices. Collaborates with Data Science and Product Engineering, leads solutions from design through implementation, and contributes individually in a fast-paced startup environment.

Build and maintain data pipelines and platform components for analytics, machine learning, and AI workloads. Implement ingestion, transformation, orchestration, testing, validation, deployment, monitoring, and troubleshooting tasks under experienced guidance. Collaborate with consultants and clients, participate in design discussions, code reviews, and agile ceremonies, document work, communicate progress, and develop foundational data engineering expertise.

Designs and optimizes ETL processes, data pipelines, databases, and AWS-based data infrastructure. Integrates complex datasets, develops Spark, Python, and SQL solutions, and builds Power BI dashboards and reporting automation. Translates business and regulatory requirements into technical designs, ensures data quality, supports production releases, and partners with compliance, analytics, and business teams. Serves as a data engineering subject matter expert and mentors less-experienced professionals, with a focus on Financial Crimes and regulatory compliance reporting.

Data Engineering Interns support data engineering and analytics initiatives by creating SQL queries, developing data models, translating business requirements into data solutions, researching data, and contributing to data remediation and data warehouse modernization efforts. The internship provides hands-on experience applying coursework in SQL, data management, and related technical fields to business needs.

Designs and scales Snowflake-based healthcare data and AI solutions, including Cortex AI agents, LLM functions, semantic search, RAG pipelines, Streamlit applications, and Snowpark data workflows. Builds ELT pipelines, optimizes Snowflake performance, implements Data Vault or Medallion architectures, and manages governance, security, HIPAA compliance, lineage, and access controls. Partners with technical and clinical stakeholders while serving as a Snowflake subject matter expert.

Build and operate large-scale data systems supporting AI training and evaluation. Responsibilities include data ingestion, transformation, quality assurance, lineage, versioning, reproducibility, and high-throughput delivery across modalities. The role requires operating petabyte-scale pipelines, applying distributed systems and data modeling expertise, supporting accelerator-based training, and collaborating cross-functionally using strong software engineering practices.

Designs, builds, and optimizes enterprise Databricks Lakehouse pipelines and data products using Spark, Delta Lake, SQL, and Python. Leads complex data initiatives, production support, governance, CI/CD, workflow orchestration, monitoring, and modernization of legacy SQL Server solutions. Partners with stakeholders, optimizes performance and cost, mentors engineers, and drives engineering standards across Azure data platforms.

Develops data acquisition strategies, integrations, migrations, workflows, architectures, and data management procedures. Builds and maintains data pipelines, ensures data quality and security, supports cloud and healthcare claims data environments, and prepares data for analytics, machine learning, and predictive modeling. Collaborates with business and technical stakeholders to define data standards, requirements, monitoring capabilities, and process improvements.

Build, test, and maintain batch and event-driven data pipelines and ETL/ELT workflows. Transform and validate large datasets, troubleshoot data-quality issues, improve workflow reliability, and support platform modernization. Collaborate with engineering, product, and data stakeholders while following strong practices for version control, testing, monitoring, documentation, and code review. The role may occasionally require work outside standard business hours.

Design, build, and optimize scalable data pipelines using Databricks, PySpark, SQL, Delta Lake, and lakehouse architecture. Collaborate with analytics, product, and engineering teams while developing batch and streaming workflows, integrating data warehouse systems, and using cloud platforms and orchestration tools such as Azure, Airflow, ADF, or Prefect.

Designs, builds, and optimizes SAP HANA-native data models and analytical objects for S/4HANA, BW/4HANA, and standalone HANA platforms. Translates business requirements into performant models, troubleshoots HANA performance issues, and applies expertise in calculation views, CDS views, AMDPs, SQLScript, replication tools, security models, and performance tuning. The role also supports analytics, migrations, upgrades, and documentation across the SAP landscape.

Migrates customer data from legacy and competitor systems into SaaS products using SQL-based tools. Performs extraction, transformation, cleanup, validation, auditing, data mapping, and quality assurance. Develops repeatable scripts and workflows, collaborates with customers and cross-functional teams, supports testing and defect resolution, and documents migration processes. The role increasingly uses BigQuery, cloud platforms, automation, and intelligent tools while supporting global delivery across regions and time zones.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Develop and maintain scalable data pipelines, ETL/ELT processes, APIs, data models, and analytics datasets using Python, SQL, Spark, and Databricks. Integrate sensitive federal health, financial, and operational data while implementing validation, governance, testing, documentation, and performance optimization. Collaborate with engineers, architects, analysts, and stakeholders to deliver reliable data products and resolve technical data needs.

Build and maintain scalable batch and streaming data pipelines, analytics warehouses, transformation layers, and dimensional data models. Perform SQL-based analysis, support dashboards and reporting, monitor data quality and delivery SLAs, troubleshoot incidents, and improve operational reliability. Collaborate with engineers, analysts, data scientists, and business stakeholders while contributing to documentation, governance, observability, and automation.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and data quality processes. Design scalable processing systems, orchestration workflows, storage patterns, and data warehouse solutions. Collaborate remotely, document technical designs, scope projects, and solve complex data engineering and data science problems.

Build and scale distributed data pipelines and architectures supporting AI products and research. Responsibilities include processing large datasets with Spark, designing AWS data systems, managing SQL and NoSQL storage, writing Python and SQL, ensuring data quality and reliability, and implementing automation, orchestration, monitoring, and scalable data operations.

Designs, develops, maintains, and optimizes the enterprise data platform, Microsoft Fabric data warehouse, ETL/ELT pipelines, integrations, data models, and reporting datasets. Ensures data quality, governance, security, compliance, and platform performance while collaborating with business intelligence and application teams. Provides technical leadership, mentoring, troubleshooting, documentation, and recommendations for improving data architecture and engineering processes. This is an on-premise role based in Bloomington.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Develop and maintain scalable data pipelines, ETL/ELT workflows, and data applications for reporting and analytics. Retrieve, parse, and process structured and unstructured data while designing and maintaining analytic solutions. Collaborate with analysts, developers, engineers, and data consumers in an agile environment. The role supports mission-driven client projects and requires eligibility for a Secret clearance.

Designs, integrates, and manages scalable batch and streaming data pipelines from internal and external sources. Responsibilities include ETL development, data schema and process enhancements, load monitoring, troubleshooting, emerging technology evaluation, privacy compliance coordination, and support for analytics, data science, and search applications. The role collaborates with business, engineering, and compliance teams while using cloud, distributed processing, containerization, and modern data architecture technologies.