Top Data & Analytics Jobs

Build and operationalize engineering data pipelines across distributed structured and unstructured sources. Implement metadata, lineage, quality, observability, cloud data services, graph modeling, reusable headless data services, and interfaces for AI retrieval and agent use. Use GCP, GitHub workflows, CI/CD, Terraform, automated testing, and production deployment practices while enforcing security, privacy, access control, and governance.

Builds and maintains Azure-based data infrastructure, including scalable ingestion pipelines, Databricks processing workflows, governance controls, data quality validation, and curated datasets for analytics and reporting. The role uses Python, PySpark, SQL, Azure Data Factory, Azure Functions, Logic Apps, and Azure Data Lake Storage Gen2, while partnering with IT, Finance, Operations, and Digital stakeholders. It is hybrid and requires several days in the Alexandria, Virginia office.

Develop dashboards, reports, and visualizations from complex datasets to support organizational decision-making. Analyze and validate data, define metrics with stakeholders, troubleshoot dashboard issues, and collaborate with data engineers, BI developers, and business partners. The role includes Agile participation, data quality monitoring, and professional development through mentorship and training.

Designs, develops, and maintains scalable cloud-based data pipelines and enterprise data solutions. The role covers data ingestion, transformation, orchestration, integration, data lakes, cloud warehouses, batch and streaming processes, performance optimization, governance, and lineage. The Senior Data Engineer collaborates with analysts, data scientists, engineers, stakeholders, contractors, and vendors while mentoring junior staff. Core tools include Databricks, dbt, Azure Data Factory, SQL, Python, and Azure.

Build and maintain scalable data pipelines and platforms using Python, ETL procedures, APIs, and software for structured and unstructured data. Develop visualizations, dashboards, and lightweight web applications to communicate insights. Collaborate with analysts, developers, and data consumers in agile teams, conduct root cause analysis, and deliver technical solutions for mission-driven clients.

Designs, builds, and supports complex ETL and data integration solutions, scalable data pipelines, enterprise data warehouses, and cloud-based data environments. Develops CI/CD pipelines, SQL procedures, batch and real-time processing, and automated testing. Troubleshoots production issues, improves reliability and performance, supports Agile delivery, advises leadership, collaborates with business partners, and mentors junior engineers.

Engineers enterprise data platforms across Snowflake, AWS, Oracle Database, and Exadata environments. Designs data architecture, replication, synchronization, ingestion pipelines, schema evolution, workload governance, high availability, disaster recovery, and performance optimization. Supports Oracle and Snowflake administration, query tuning, data modeling, QA readiness, project planning, technical strategy, risk management, and mentoring. Requires expertise with large-scale databases, Oracle replication technologies, and resilient data platforms.

Leads and mentors an interdisciplinary team developing ETL pipelines and data infrastructure for AI/ML and LLM applications. Builds, normalizes, curates, and maintains complex datasets; implements machine learning algorithms; improves scalability, automation, and data delivery; and develops software using Agile methodologies. The role requires an active TS/SCI clearance with a recent CI polygraph and supports national security technology programs.

Build and operate a greenfield data platform using Medallion Architecture, integrating SQL Server and Oracle sources. Develop dbt and Python transformations, Airflow pipelines, Iceberg tables, business data marts, metadata and lineage capabilities, and an MCP server for AI-agent data access. Migrate Alteryx workflows, automate Excel reporting, and support governed analytics and AI adoption.

Build and maintain data pipelines and scalable cloud infrastructure supporting analytics and machine learning. Integrate internal and external data sources, ensure data integrity, contribute to analytical investigations, document findings, and apply data governance, security, and performance optimization practices while collaborating with senior engineers and cross-functional teams.

Design, build, and scale enterprise data products, pipelines, and platform capabilities using Databricks, Spark, Python, SQL, and cloud technologies. Modernize analytical solutions into governed production systems while implementing data quality, observability, lineage, security, monitoring, and governance. Partner with product, analytics, architecture, and business teams on technical design and complex data initiatives. Optimize platform performance and reliability, troubleshoot issues, establish engineering standards, and mentor other engineers.

The role involves designing and managing data pipelines, developing canonical datasets, and ensuring data integration and compliance. Collaborate across teams to meet data needs and maintain data architecture.

Owns the data transformation layer powering agency reporting. Builds reliable analytical models, dimensional schemas, reporting architecture, monitoring, and alerting. Translates government and customer reporting requirements into reusable data models and tools, supports embedded analytics and recurring imports, and ensures authorization scoping prevents cross-organization data exposure. Partners across product and implementation teams while establishing data engineering practices and documentation.

Leads data architecture and engineering for the META initiative, owning solution design, data modeling, pipeline standards, code reviews, mentoring, performance tuning, and roadmap decisions. Requires deep AWS data engineering expertise, PySpark, SQL, infrastructure as code, and CI/CD experience.

Build and optimize batch and streaming data pipelines on Google Cloud for analytics and machine learning. Develop Python, SQL, Beam, and Spark transformations; deliver curated and ML-ready datasets; implement data quality, governance, security, monitoring, and schema management; optimize BigQuery; maintain Airflow DAGs; and manage Terraform-based infrastructure and CI/CD. Collaborate with architects, data scientists, ML engineers, and stakeholders while troubleshooting pipeline and data quality issues.

Build and optimize batch and streaming data pipelines for analytics and AI workloads using GCP services, Python, SQL, Beam, and Spark. Develop ML-ready datasets, implement data quality, governance, security, monitoring, and schema management, and optimize BigQuery performance. Maintain Airflow DAGs, Terraform infrastructure, and CI/CD pipelines. Troubleshoot production issues and collaborate with architects, data scientists, ML engineers, and stakeholders. The role requires U.S. citizenship, a bachelor’s degree, Public Trust eligibility, and 3–6+ years of experience.

Develop scalable data pipelines, high-performance SQL queries, reports, dashboards, and analytics solutions for a federal law enforcement client. Integrate disparate data sources, use relational and cloud databases, support evolving stakeholder requirements, and communicate technical solutions to clients and cross-functional teams. The role also involves data governance, quality improvement, mentoring, technical leadership, and consulting engagement. Employment is contingent upon contract award and requires U.S. citizenship plus eligibility for DHS 6C Public Trust.

Designs and builds data models, schemas, and ETL/ELT pipelines for analytics, reporting, and machine learning workloads. Optimizes query performance and cloud data infrastructure, establishes data quality and governance practices, integrates datasets with BI and machine learning tools, documents workflows, and mentors junior engineers. Collaborates with data scientists, analysts, and product owners to translate business requirements into scalable technical solutions.

Engineers and maintains enterprise telemetry ingestion pipelines, edge processing, data routing, filtering, masking, tagging, and transformation solutions. Develops Splunk technology add-ons, resolves parsing and timestamping issues, establishes secure transport pathways, optimizes storage and licensing, and documents data flows and taxonomies. Supports platforms including TAK, TRAX, Compass, CDS, and cloud environments while enabling threat hunting, compliance reporting, and DoD data visibility requirements.

Designs, builds, deploys, and maintains scalable data platforms and ETL/ELT pipelines. Processes structured, unstructured, batch, streaming, and event-based data while optimizing data lake architecture and cloud performance. Collaborates with analysts, engineers, developers, and data consumers on mission-driven projects. Responsibilities include data modeling, governance, cataloging, lifecycle management, automation, platform tuning, and developing data solutions for applications such as fraud detection, cancer research, and national intelligence.

Build and maintain Snowflake and dbt data pipelines, dimensional models, integrations, and Python workflows. Own data quality, testing, monitoring, documentation, performance, and production support. Translate stakeholder needs into reliable data products, develop internal tools and APIs, and follow Git, CI/CD, testing, and release practices. Work independently within Scrum teams, resolve escalated issues, contribute to standards, and mentor junior engineers.

Entry-level data platform engineer responsible for building and maintaining SQL and dbt transformations, Python workflows, ETL/ELT pipelines, integrations, automated tests, and data-quality checks. The role includes monitoring and troubleshooting pipelines, supporting applications and production issues, documenting technical processes, and collaborating with senior engineers, analysts, and business stakeholders. The position offers mentorship while developing skills across Snowflake, cloud data services, software development, and data operations.

Leads the design and development of Azure and Databricks-based data engineering solutions for ingesting, transforming, storing, and distributing big data. Oversees engineering teams, sets technical strategy, mentors junior staff, develops REST APIs hosted in Kubernetes, performs testing, and collaborates with Product, Data Science, Architecture, and business stakeholders.

Lead complex data engineering engagements, manage stakeholders and risks, coordinate offshore teams, and build scalable Azure data pipelines. Develop ingestion and transformation solutions using Azure Databricks, Data Factory, Spark, Delta Lake, SQL, and Python. Harmonize raw data into consumer-ready formats, query large datasets, support on-premises cloud migration, and collaborate with executives and delivery teams.

Own end-to-end data engineering for a large-scale analytics and machine learning platform. Build ingestion, modeling, transformation, and workflow pipelines; integrate external systems; ensure reliability, scalability, and data quality; and support production machine learning workflows. Develop programmatic interfaces for AI applications and manage analytics engineering within a cloud data warehouse.