Top Data & Analytics Jobs

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.

Designs and leads manufacturing data architecture, repositories, models, integration patterns, pipelines, data services, and APIs. The role integrates complex data from equipment, sensors, quality, engineering, manufacturing, and enterprise systems; establishes standards and technical direction; and communicates risks and tradeoffs to senior stakeholders. Requires advanced SQL, programming, relational modeling, greenfield solution delivery, Secret or Top Secret clearance eligibility, flexible shift availability, and at least 10% travel.

Develop and deploy scalable data pipelines and platforms that organize structured and unstructured data for mission-driven client projects. Collaborate with analysts, developers, data scientists, infrastructure engineers, and data consumers in agile environments. Support data platform assessment, design, development, maintenance, ETL operations, analytics workloads, and data examination. The role requires Python, database, source control, automation, and operating-system knowledge, plus a Secret clearance and bachelor’s degree.

Architects, deploys, and operates large-scale Apache Kafka and Confluent streaming platforms. Responsibilities include cluster sizing, configuration, security, automation, observability, HA/DR, troubleshooting, and developer enablement. Partners with application and cross-functional teams to deliver reliable event-driven solutions, translates ambiguous requirements into production-ready designs, contributes to code and design reviews, and mentors junior engineers.

Leads the architecture, development, and production operation of complex data pipelines and platforms. Resolves scalability, data quality, reliability, and architectural challenges; evaluates data technologies; mentors engineers; establishes engineering standards; partners with product, analytics, and operations teams; communicates technical trade-offs; and conducts proofs of concept to guide platform strategy.

Designs and maintains scalable data platforms, backend services, APIs, ingestion pipelines, and data quality frameworks. Responsibilities include transforming heterogeneous datasets, supporting NLP and analytics workflows, modernizing legacy processing systems, monitoring pipeline reliability, and maintaining data lineage and documentation. The role also supports cloud-native architecture, governance, security compliance, and NIH Public Trust requirements.

Build and maintain data integration infrastructure, pipelines, data models, governance practices, and scalable analytical solutions. Translate business problems into data-driven solutions, identify and transform complex data sources, develop and test code, support production applications, and guide junior team members. Collaborate with stakeholders on business cases, data strategy, quality, architecture, documentation, and governance while applying modern distributed technologies and software engineering best practices.

Principal Data Engineer responsible for translating business problems into data solutions, designing and maintaining scalable ETL pipelines and data warehouses, managing data quality and governance, conducting exploratory and statistical analysis, and developing dashboards and visualizations. The role requires collaboration with stakeholders, mentoring junior engineers, cloud platform expertise, data cleansing, query optimization, experimentation, and machine learning model development in a retail and digital advertising context.

Builds scalable data pipelines, ETL frameworks, data models, and cloud-based analytics solutions. Extracts, transforms, validates, and analyzes data; optimizes Spark applications; designs data warehouses and marts; and addresses system integration challenges. Develops and tests code, deploys solutions, documents processes, and supports governance. Collaborates with product and data teams, contributes to CI/CD and workflow orchestration, and provides guidance to less experienced programmers.

Build and maintain scalable fraud data pipelines, Snowflake data marts, dbt models, and summarized metric tables. Partner with fraud analysts and engineering teams to enable reporting, scorecards, monitoring, and operational decision-making. Establish data engineering standards, documentation, testing, governance, data quality, and pipeline reliability for audit-ready fraud analytics infrastructure.

Build and operate production data pipelines from clinical, operational, billing, and product systems into trusted warehouse datasets. Responsibilities include ingestion with dlt and Prefect, data modeling in dbt, AI training and evaluation datasets, pipeline testing and monitoring, lineage and alerting, schema changes, backfills, and secure handling of PHI through access controls, de-identification, and audit trails.

Designs, implements, and operates secure data management systems supporting intelligence missions. Develops data architectures, storage and integration strategies, databases, pipelines, and visualization capabilities. Optimizes data throughput and query performance, maintains certified environments, supports data science and IT activities, evaluates emerging technologies, and reports performance indicators to leadership. Requires active TS/SCI clearance with polygraph and U.S. citizenship.

Build and maintain scalable ETL/ELT pipelines, integrate structured and unstructured data, create curated datasets, and enforce data quality, lineage, governance, security, and privacy controls. Develop data models, documentation, and NLP-ready data while supporting visualization, analytics, and MLOps deployments. Collaborate with data scientists, clinicians, government personnel, and program staff to translate analytical needs into reliable data solutions.

Leads a backend data engineering team supporting SOCOM intelligence missions. Designs scalable SQL databases, ETL/ELT pipelines, APIs, cloud database platforms, and data integrations. Uses Python, SQL, Airflow, Docker, Kubernetes, and AWS while maintaining security controls and testing software. Mentors junior staff, manages technically focused teams, supports data modeling and validation, and contributes to complex data engineering initiatives.

Build and maintain scalable ETL/ELT pipelines, integrate structured and unstructured government data, and produce curated datasets for analytics and machine learning. Ensure data quality, lineage, governance, security, and privacy compliance while documenting models and transformations. Support NLP, LLM, visualization, and MLOps initiatives; monitor workloads, resolve pipeline failures, and provide technical guidance to data scientists and government stakeholders.

Designs and optimizes Power BI dashboards, semantic models, datasets, DAX measures, and reporting solutions for identity governance and administration analytics. Partners with ETL engineers and stakeholders to validate Parquet data from Azure Synapse and SailPoint sources, establish data-quality checks, monitor refreshes, define KPIs, and document data lineage. Supports compliance reporting, access governance, audit readiness, certification metrics, and Zero Trust analytics.

Build and own Databricks-based data pipelines, lakehouse models, and PostgreSQL serving layers for an AI-powered healthcare interview platform. Responsibilities include batch and incremental processing, data quality, observability, governance, privacy controls, infrastructure as code, CI/CD, and productionizing analytical work. The role requires Python, SQL, Databricks, PostgreSQL, Azure, and analytics data modeling expertise, with collaboration across engineering, data science, and product teams.

Designs, develops, and optimizes scalable data pipelines, dataflows, schemas, and ETL artifacts using AWS and distributed data technologies. Partners with business, technical, and government stakeholders to deliver data solutions, support deployed applications, resolve technical issues, and maintain data governance. The role also contributes to product vision and roadmaps, collaborates with a government product team, documents architectures, and provides occasional off-hours deployment support.

Build and maintain scalable data platforms and pipelines supporting AI-driven drug discovery. Develop ingestion, transformation, storage, orchestration, streaming, validation, versioning, lineage, and reproducibility capabilities for chemical, biological, laboratory, and experimental data. Partner with AI scientists, engineers, researchers, and laboratory teams to deliver accurate, traceable datasets for machine learning and scientific workflows. Operate production data systems, optimize performance, and ensure reliable integration across instruments, databases, and cloud environments.

Build and maintain scalable Snowflake-based ELT/ETL pipelines, production data models, and observability frameworks. Translate business requirements into reliable solutions supporting analytics, BI, and ML. Establish data quality, governance, lineage, security, and compliance practices while optimizing warehouse performance, cost, and data freshness. Collaborate with Analytics, Product, and Engineering teams, contribute to architecture decisions, evaluate Snowflake tooling, and use AI-assisted development tools to accelerate delivery.

Designs, builds, and maintains scalable cloud and on-premises data architectures and ETL/ELT pipelines. Ensures data quality, security, lineage, reliability, and performance while integrating structured and unstructured sources. Prepares datasets for AI/ML, semantic search, analytics, and mission applications. Collaborates with analysts, AI engineers, software engineers, and business teams to deliver production-ready data, automate workflows, troubleshoot pipelines, and optimize delivery costs.

Designs, builds, and optimizes scalable data collection, storage, access, and analytics systems. Creates and automates reliable data pipelines, supports data quality and governance, ensures SLA and performance compliance, and monitors pipeline operations. The role requires collaboration with analytics, business, and customer teams, with potential work involving AI/ML pipelines, Databricks, DevOps practices, streaming data, and event-driven architectures.

Build and operate production data pipelines, CDC streams, and Snowflake Bronze-layer infrastructure using Python, SQL, Redpanda, and Dagster. Maintain source adapters, historical snapshots, schema-drift detection, deduplication, data-quality checks, reconciliation, and observability. Collaborate on Silver-layer modeling, governance tagging, CI/CD, containerization, and HVR-to-Redpanda migration. Participate in incident response and develop deeper ownership of orchestration, streaming, data governance, and dbt-based layered modeling.

Develop and modernize data solutions in Microsoft Fabric by migrating SQL Server datamarts, building PySpark and SQL pipelines, tuning Spark workloads, implementing incremental processing, and creating dimensional models. Ensure data quality through automated testing and reconciliation, support secure CI/CD deployments, troubleshoot production issues, and collaborate with technical and business stakeholders.