Top Data & Analytics Jobs

Build scalable production-grade ETL/ELT pipelines on Databricks using PySpark, Spark SQL, Delta Lake, Delta Live Tables, and Workflows. Develop data ingestion, transformations, quality rules, orchestration, monitoring, and automation across Bronze, Silver, and Gold layers. Apply dimensional data modeling, streaming, cloud platforms, CI/CD, governance, security, performance tuning, and lakehouse architecture. Explain technical concepts and architecture decisions using real project examples.

Lead design, build, and maintain scalable data pipelines, ETL processes, data models, and storage solutions. Support production data ecosystem, optimize performance, ensure data quality and security, integrate data sources/APIs, and provide technical guidance to teams while collaborating with stakeholders.

The Lead Data Engineer will design, implement, and maintain data pipelines for mission-relevant data, ensuring compliance with security frameworks and optimizing data accessibility and interoperability across mission systems.

Design and maintain production data pipelines and architecture across Snowflake and Databricks, scale Terraform deployments, implement data governance, build demo data processes, and collaborate cross-functionally to support analytics and product use cases.

Design, build, and operate production data pipelines and architectures across Snowflake and Databricks. Scale demo data across cloud warehouses, partner with infrastructure for high availability, implement data governance, and collaborate cross-functionally to support analytics and GTM use cases.

As a Data Engineer, you will manage data pipelines, automate processes, and collaborate on software solutions to optimize supply chain performance.

Lead the data engineering strategy and architecture for distributed pharmaceutical data systems. Design and scale pipelines, APIs, and data platforms while ensuring reliability, governance, quality, and PII/PHI protection. Drive technical decisions, improve developer efficiency, deliver business impact, influence cross-functional roadmaps, mentor engineers, communicate with executives, and promote organizational adoption of new technologies and processes.

Develop and support enterprise business intelligence, data warehouse, ETL, reporting, dashboard, and analytics solutions. Build optimized SQL and PL/SQL queries, troubleshoot applications and data issues, support system interfaces and testing, participate in deployments, and provide on-site technical support to government users and senior leadership. Maintain documentation, perform root cause analysis, and implement improvements for system reliability and performance.

Supports the Army’s cPROBE data repositories and database layer by designing, implementing, and maintaining reliable, secure, high-performance data infrastructure. Responsibilities include managing interfaces and APIs, minimizing data access latency, defining data and metadata requirements, designing data warehouses, implementing business rules, facilitating change control, and conducting data verification and testing. The role also coordinates with architects, programmers, analysts, and engineers while providing product support and maintenance.

Leads enterprise data engineering and AI-ready data knowledge initiatives across Network Services. Designs scalable pipelines, data models, metadata, governance, lineage, quality controls, and validation frameworks for structured and unstructured data. Partners with subject matter experts to prepare trusted context for AI and agentic workflows, guides hybrid data architectures, supports testing and releases, establishes control standards, and mentors data engineers.

Lead design, build, and maintain enterprise-scale data platforms and pipelines. Mentor cross-functional teams, develop software to process structured/unstructured data, and operationalize ETL/ELT workflows for analytics in agile environments supporting mission-driven projects.

Designs and owns analytics-ready datasets, semantic views, ELT pipelines, and standardized metrics in Snowflake. Partners with business, analytics, and engineering teams to enable self-service BI, maintain data quality and documentation, and support forecasting, machine learning, and generative AI initiatives. Provides senior guidance on data modeling, analytics engineering practices, and scalable data product design.

Design, build, and maintain scalable data pipelines, lakehouse/data‑warehouse architectures, and ETL/ELT workflows to support analytics and AI/ML. Ensure data quality, lineage, monitoring, and SLA-driven reliability; support project delivery, client communication, and business development under project management guidance.

Design and implement scalable data models and ETL pipelines (batch and streaming), ensure data quality/SSOT, collaborate with product and platform teams to resolve issues, contribute to data governance, and explore practical integrations between data engineering and AI.

Build and operate scalable data pipelines and services to power internal products. Design data models, curate high-quality datasets, collaborate with cross-functional teams, and mentor engineers while improving platform reliability, observability, and AI-ready data layers.

Build and lead Way’s data infrastructure from the ground up. Design cloud-native data lakes, scalable batch and real-time ETL/ELT pipelines, ingestion frameworks, data warehouses, orchestration, streaming systems, data quality monitoring, and DataOps practices. Develop Python and SQL-based data solutions, infrastructure as code, feature stores, dashboards, and self-service analytics. Partner with product and business teams on metrics, predictive modeling, and experimentation while establishing data-driven practices.

Develops and administers complex ETL and ELT data integration solutions using Microsoft Fabric, SQL Server, Lakehouses, Warehouses, pipelines, notebooks, and SSIS. Designs reliable data flows, consumption views, semantic datasets, and enterprise data models; troubleshoots data quality and pipeline issues; supports database administration, backup and recovery, reporting, deployment, and CI/CD. Collaborates with business, analytics, technical, vendor, and reporting teams to deliver secure, scalable, trusted data products.

Provide data engineering and analysis consulting to a Department of Defense client. Responsibilities include data modeling, data integration, software development, collaboration with clients and technical teams, and delivery of high-quality data solutions. The role requires an active Secret clearance, a bachelor’s degree, and at least eight years of relevant experience.

Develop and maintain data integration, transformation, modeling, and database processes across Azure SQL Database and Azure Cosmos DB. Diagnose data issues, optimize data structures, support CI/CD and production operations, and establish data standards and documentation. Collaborate with business and technical stakeholders to define requirements, service levels, and quality expectations within an Agile development environment.

Design, build, and maintain cloud data infrastructure and ETL/ELT pipelines using Microsoft data technologies. Develop and optimize T-SQL scripts and star-schema data models, support data quality, governance, and security, and participate across the full lifecycle from requirements to deployment and monitoring.

Design and implement scalable data architecture and storage (cloud/on-prem), build and maintain ETL pipelines, enforce data governance and security, ensure data quality, evaluate technologies, collaborate with analysts and data scientists, and document data assets.

Build and maintain scalable pipelines to ingest, clean, and integrate large multimodal datasets. Own ingestion across APIs, files, cloud storage, and warehouses. Design linkage, entity resolution, deduplication, and schema harmonization. Implement data quality checks, validation, and documentation. Collaborate with engineering, research, and deployment teams to prepare integrated data for simulations and evaluate new data sources.

Senior consulting data engineer responsible for leading complex data engineering projects, coding robust solutions, designing complex data models, troubleshooting and optimizing data operations, mentoring engineers, interfacing with customers and cross-functional teams, and ensuring quality through testing, code reviews, and performance tuning to deliver on-time, on-budget solutions.

Build and maintain data pipelines and dbt models, own reporting and dashboards across Finance, GTM, Product, and Engineering, support third-party financial data integrations, enable GTM and customer-facing analytics, and ensure data quality, testing, and documentation.

Lead deployment and administration of the electronic Quality Management System (eQMS), digitize quality workflows, and build analytics and BI solutions (Power BI) to monitor quality KPIs. Partner cross-functionally to implement automation, support UAT/validation/training, maintain data integrity and governance, and enable future predictive quality and AI-enabled decision support.