Top Data & Analytics Jobs

Designs and deploys scalable data pipelines, preprocessing workflows, feature-engineering services, and machine learning models for secure DoD environments. The role supports structured and unstructured mission data, exploratory analysis, visualization, model inference APIs, reproducible MLOps workflows, and decision-support capabilities. Responsibilities include collaborating with product, software, platform, DevSecOps, data science, and mission teams; deploying containerized services; participating in Agile delivery; and maintaining technical documentation.

Design, develop, deploy, operationalize, and maintain enterprise-scale data applications, pipelines, and platforms. Process structured and unstructured data, build scalable ETL and ELT workflows, convert data into usable formats, and support monitoring and operation of data platforms. Collaborate with analysts, developers, engineers, and data consumers in an agile environment to deliver mission-driven solutions.

Designs and maintains scalable data pipelines using Databricks, Spark, and cloud services. Builds transformation workflows, integrates APIs, databases, and cloud storage, and supports analytics and reporting. Implements data validation, monitoring, observability, governance, security, and automation. Collaborates with data science, analytics, and business teams to deliver reliable data solutions in AWS and other cloud environments.

Designs and manages large-scale structured and unstructured data pipelines in AWS. Builds RAG pipelines involving document ingestion, embeddings, indexing, and retrieval; orchestrates ETL/ELT workflows with Prefect and Apache NiFi; and optimizes Elasticsearch systems. The role supports analytics, ML, and application workloads while ensuring data quality, governance, lineage, security, observability, and reliability. Collaboration with architecture, ML, and application teams and troubleshooting distributed systems are also required.

Build strategic data infrastructure roadmaps, design and implement complex ETL/ELT pipelines, and deliver scalable, secure data solutions. Collaborate across teams on cloud platform selection, data management, governance, privacy, and compliance. The role requires expertise in Databricks, Snowflake, data modeling, performance optimization, and pipeline automation.

Designs, builds, and maintains real-time and near-real-time data ingestion pipelines using Apache NiFi, REST APIs, and scripting. Ensures reliable cross-domain data transfers, data quality, observability, security, and compliance. Parses and transforms structured and unstructured data, develops monitoring and alerting automation, documents system configurations, and collaborates with data, platform, software, security, and governance teams. The role is on-site in Honolulu and supports mission-critical operational systems.

Designs, builds, and maintains real-time and near-real-time data ingestion pipelines using Apache NiFi, REST APIs, scripting, and streaming technologies. Monitors data quality, pipeline health, observability, reliability, and performance while moving classified data across security domains. Develops automation for validation and alerting, documents data flows and procedures, and collaborates with engineering, platform, analytics, security, and governance teams to ensure compliant, trustworthy data delivery.

Develop and maintain large-scale, cloud-native data services and distributed pipelines for multi-tenant healthcare systems. Responsibilities include data modeling, database component development, SQL programming, performance tuning, testing, deployment, code reviews, proof-of-concepts, and full software development lifecycle ownership. Collaborate with global engineering teams, evaluate technologies, apply security and operational requirements, and use AI tools to accelerate development.

Lead data engineering projects by designing scalable data solutions, pipelines, ETL processes, cloud-native applications, and ML-enabled analytical tools. Responsibilities include architecture design, code reviews, CI/CD, documentation, mentoring engineers, client engagement, proposal support, and collaboration with consulting teams. The role requires strong Python, SQL, cloud, big-data, orchestration, and DevOps expertise, with international client travel as needed.

Lead data engineering initiatives involving large-scale data solutions, cloud-native applications, ETL pipelines, data architectures, CI/CD, and machine learning deployment. The role includes mentoring engineers, ensuring code quality, documenting operational processes, supporting consulting teams, engaging clients, contributing to proposals, and delivering analytics solutions globally. International client travel may be required.

Supports the development, maintenance, and monitoring of scalable ETL/ELT data pipelines in secure environments. Responsibilities include ingesting, cleaning, transforming, and validating structured and unstructured data; working with relational and NoSQL databases; building Docker modules; performing security scans and unit tests; and participating in Agile ceremonies and code reviews.

Architects and leads enterprise-scale data platforms, data mesh topologies, lakehouse infrastructure, and real-time analytics for classified environments. Establishes data architecture strategy, security and encryption standards, zero-trust governance, multi-cloud and air-gapped deployment strategies, and enterprise data modeling practices. Serves as the principal technical authority and advises government and executive stakeholders on mission-critical data capabilities.

Leads architecture, scaling, governance, and implementation of enterprise data platforms across multi-cloud and air-gapped environments. Designs batch and streaming systems, optimizes Spark and Trino/Presto clusters, implements Terraform-based infrastructure, establishes data quality, lineage, security, and compliance controls, and leads Agile engineering pods while mentoring staff. Requires advanced expertise in distributed data systems, cloud platforms, Kubernetes, programming, and regulated defense environments.

Design, build, and own scalable ETL/ELT pipelines, data models, and data infrastructure. Improve data quality, observability, performance, and CI/CD for data workflows. Partner with analytics, ML, software, and product teams, mentor engineers, and resolve complex data issues.

Design, build, and maintain scalable batch and real-time data pipelines and data lake architecture. Improve observability and SLOs, optimize ETL/ELT, develop dbt workflows, support event-driven architectures, integrate financial APIs, and collaborate with analytics and ML teams to deliver reliable, model-ready data products.

Designs and deploys scalable data pipelines, preprocessing workflows, feature-engineering services, and machine learning models for secure Department of Defense environments. Develops reproducible, tested, containerized data and ML services using Python, Spark, Databricks, Docker, Terraform, SageMaker, and MLflow. Performs exploratory analysis, visualization, data storytelling, model evaluation, and decision-support development while collaborating with product, software, platform, DevSecOps, data science, and mission teams.

Participate in an internship focused on database modeling and optimization, data analysis and consolidation, data quality, and data support. Work alongside experienced professionals on real-world projects while developing technical and professional skills. The program requires prior knowledge of data concepts, spreadsheets, databases, programming logic, and analytical problem-solving.

Design, build, and operate end-to-end ETL/ELT pipelines to ingest network telemetry into a Data Mart. Create high-performance dimensional models, implement automated data quality and observability, enforce governance and IAM, maintain metadata and lineage, and enable analysts with production-ready datasets and standardized metrics.

Design, build, and maintain scalable Spark/Python data pipelines and automated workflows. Enforce data governance, quality, lineage, and observability. Support ML pipelines (feature engineering, validation, monitoring) and document data assets, contracts, and standards.

Lead technical strategy, architecture, and modernization of enterprise master data and data lakehouse platforms. Design and implement large-scale data pipelines, databases, and distributed systems using Python, Spark, Redshift, Iceberg and AWS services. Ensure performance, reliability, governance, CI/CD, and AI-enabled engineering practices while mentoring senior engineers and aligning cross-functional stakeholders.

Design, build, and operate large-scale, fault-tolerant real-time and batch data pipelines powering an ad-tech DSP. Collaborate with engineers and data scientists to define data models and SLAs, optimize performance and capacity, enforce data quality and monitoring, mentor engineers, and evaluate new data technologies to scale analytics and ML workloads.

Leads data engineering strategy and mentors an analytics engineering team. Designs scalable ELT pipelines, warehouse integrations, data models, API ingestion workflows, and data quality frameworks using cloud and database technologies. Conducts exploratory analysis, supports dashboards and analytical solutions, monitors warehouse performance and costs, and ensures data governance, security, and compliance. Collaborates with data scientists, analysts, engineers, and business stakeholders to deliver healthcare analytics solutions.

Own Mercor’s data platform infrastructure across Snowflake, Fivetran, Airflow, Astronomer, and Hex. Responsibilities include data organization, role-based access control, row-level security, PII masking, governed migrations, pipeline reliability, on-call support, dashboard governance, warehouse cost optimization, and CDC or streaming ingestion. The role also establishes platform standards and builds internal tooling for a new Data Platform team.

Build and own the unified data layer: ingestion pipelines into ClickHouse, data modeling and entity resolution, canonical metric definitions, quality checks, and internal data tools. Support ad-hoc analyses and work closely with engineering to ensure stable, trustworthy data powering customer dashboards and product BI.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.