Top Data & Analytics Jobs

Develops data pipelines, data models, data warehouses, and data flows using Google Cloud Platform, BigQuery, Spark, Kafka, Hive, and related technologies. Performs data extraction, quality checks, integration analysis, testing, deployment, governance support, and troubleshooting. Creates logical and physical data models, automates jobs, documents solutions, and supports end users and less experienced programmers. Provides technical guidance and contributes to business problem-solving through analytics and automation.

Designs, builds, and supports scalable cloud data platforms, pipelines, and applications. Responsibilities include data modeling, distributed processing, lakehouse architecture, data warehousing, security controls, observability, and real-time data solutions. The role collaborates with product and engineering teams, influences technical standards, communicates data outcomes, and mentors data professionals while delivering resilient solutions using technologies such as Python, Spark, Databricks, and Snowflake.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Collaborate with data scientists, analysts, product managers, and software engineers while applying distributed data, lakehouse, streaming, database, and security technologies. Establish reusable engineering patterns, ensure performance and resilience, communicate technical outcomes, mentor team members, and deliver secure data solutions with minimal supervision.

Designs, builds, and operates scalable cloud data platforms, pipelines, and applications. The role uses Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases, and public cloud technologies. Responsibilities include data modeling, distributed data processing, security controls, architecture standards, observability, stakeholder communication, mentoring, and delivering resilient data solutions in collaboration with product, engineering, analytics, and Agile teams.

Build and architect scalable cloud data platforms, pipelines, and applications. Design distributed, real-time, and batch data solutions using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring team members and communicating technical outcomes to stakeholders.

Design, build, and operate scalable cloud data platforms and pipelines using Python, Spark, SQL, Databricks, Snowflake, and relational or NoSQL databases. Develop distributed, real-time, and secure data solutions; enforce engineering standards; collaborate with product, engineering, analytics, and machine learning teams; communicate technical outcomes; and mentor data professionals.

Build and maintain observability across enterprise data pipelines, including data quality checks, freshness, volume, schema monitoring, alerting, incident triage, root cause analysis, SLA management, and runbooks. Configure tools such as Monte Carlo, Great Expectations, CloudWatch, and Datadog to improve pipeline reliability and data trust.

Designs and builds AWS data ingestion pipelines for structured and unstructured data supporting analytics and AI/GenAI use cases. Responsibilities include developing data lake ingestion frameworks, preparing AI-ready datasets through document parsing, chunking, and embedding pipelines, and creating reusable metadata-driven solutions. Requires AWS services, Python, and PySpark experience, with Bedrock, vector databases, and OpenSearch as preferred skills.

Develop and deploy scalable data pipelines and platforms that organize structured and unstructured data from disparate sources. Perform data manipulation, ETL, API querying, data retrieval, parsing, and processing. Create visualizations, dashboards, and lightweight web applications to communicate insights. Collaborate with analysts, developers, and data consumers in an agile environment while supporting platform design, development, maintenance, and technical problem-solving.

Develops and maintains SQL-based data pipelines, ETL processes, migrations, EDI 834 benefit files, custom reports, and dashboards. Builds AI-powered skills, agents, automations, and MCP integrations with secure access to internal data. Manages Azure DevOps work items, supports compliance and data security, collaborates with technical and non-technical stakeholders, and delivers reliable client solutions.

Build and maintain scalable consumer data pipelines on Databricks using PySpark, Spark SQL, and Delta Lake. Implement medallion architecture, Unity Catalog, and Workflows. Optimize cluster performance and costs, and monitor and troubleshoot production jobs.

Build and support AWS-based ETL pipelines for regulatory and reporting data within the NURO program. Translate business reporting requirements into data models, emphasizing data lineage, auditability, accuracy, governance, and GxP-aware practices. Collaborate with business subject matter experts and support data processing using AWS Glue, Redshift, S3, Athena, SQL, and Python.

Develops and operates an AWS-based data platform supporting affiliate markets. Responsibilities include building ingestion and transformation pipelines, harmonizing multi-country data into common models, and providing production support, monitoring, and break-fix services through a shift rotation.

Designs and implements data infrastructure, ETL pipelines, transformation logic, and integration solutions for defense analytics environments. Integrates IoT streams, APIs, and legacy systems; develops validation and data-quality checks; optimizes reliability and performance; and maintains data lineage documentation. The role supports defensive cyberspace operations and AI/ML security capabilities using technologies including Airflow, NiFi, Spark, Kafka, Hadoop, Python, SQL, and Scala.

Designs, develops, and maintains enterprise data integrations, ETL/ELT pipelines, and analytics infrastructure using Informatica and Snowflake. Partners with data scientists, analysts, IT, and business stakeholders to deliver trusted data for reporting, forecasting, business intelligence, and data science. Responsibilities include platform administration, security and access management, troubleshooting, data governance, quality initiatives, architecture reviews, and continuous improvement.

Designs, develops, optimizes, and maintains scalable data workflows and pipelines using Matillion, HVR, and Databricks. Performs data transformation, modeling, SQL development, analytics, and troubleshooting while ensuring data quality, security, governance, and compliance. Collaborates with data scientists, analysts, architects, and business data owners to translate requirements into technical solutions and documents data processes. The role requires substantial data engineering experience, U.S. citizenship, and an active DoD Secret clearance.

Supports data engineering through code debugging, troubleshooting, solution development, peer code reviews, and system performance improvements. The intern collaborates with technical teams, reviews documentation, learns emerging technologies, and communicates solutions to team members and business leaders. The role requires current enrollment in a relevant bachelor's or higher degree program, a minimum 3.0 GPA, and programming or version-control experience. The internship is hybrid and lasts 11 weeks.

Designs and maintains secure data pipelines, ETL processes, data models, integrations, and analytics infrastructure across AWS GovCloud environments. The role integrates structured and unstructured mission data, supports migration and synchronization, automates workflows through DevSecOps and CI/CD, and ensures data quality, security, compliance, and performance. Responsibilities also include documentation, metadata management, dashboard support, troubleshooting, and Agile collaboration within a DoD modernization initiative.

Designs, builds, deploys, and operates production data pipelines, lakehouses, warehouses, data models, and streaming workloads across Microsoft Fabric and Azure. Develops SQL and PySpark transformations, ingestion frameworks, data quality controls, security, governance, monitoring, and CI/CD automation. Partners with analytics, BI, AI, security, and business teams to deliver trusted datasets for reporting, data science, and RAG applications. Optimizes performance, cost, reliability, and data freshness while documenting architecture and resolving operational issues.

Leads data architecture and engineering modernization across business domains. Designs and implements complex pipelines, integrations, data models, and orchestration using Snowflake, AWS, and Airflow. Establishes data quality, governance, security, testing, observability, CI/CD, and reliability practices. Provides architectural guidance, evaluates technical solutions, leads cross-team initiatives, mentors engineers, and improves platform performance and cost efficiency.

Build and maintain production ETL pipelines for structured and unstructured data, integrate machine learning models for enrichment, develop data transformations and quality monitoring, design warehouses and dimensional models, establish governance frameworks, and contribute to data architecture and platform improvements. Collaborate with data scientists, analysts, and engineering teams to deliver reliable, actionable intelligence.

Designs and maintains scalable data pipelines, workflows, transformations, dashboards, and applications within Palantir Foundry. Integrates and governs structured and unstructured data, resolves quality issues, develops reusable code and Ontologies, and translates stakeholder requirements into technical solutions. Collaborates in Agile teams using Jira and supports operational decision-making through data visualization and workflow tools.

Designs and maintains scalable data pipelines, workflows, transformations, dashboards, and applications within Palantir Foundry. Integrates and governs structured and unstructured data, resolves quality issues, develops reusable code and Ontologies, and translates stakeholder requirements into technical solutions. Collaborates in Agile teams using tools such as Jira while supporting operational decision-making and public health outcomes.

Leads data engineering initiatives for mission-driven client projects. Oversees the design, development, deployment, and maintenance of scalable Databricks platforms and data pipelines. Mentors multidisciplinary teams, guides Agile delivery, performs data exploration, and applies governance, testing, CI/CD, Lakehouse architecture, dimensional modeling, and AWS expertise to produce reliable data products.

Lead data engineering role responsible for administering SQL Server and Amazon Redshift, building Python ETL/ELT pipelines, developing data warehouse models, and preparing data for AI and ML applications. The role includes database performance tuning, AWS data platform security, workflow orchestration, CI/CD, production support, release management, and on-call ownership. Collaboration with product and marketing teams supports modernization of data environments and broader analytics adoption.