Top Data & Analytics Jobs

Lead production data engineering workstreams for AI-enabled financial services solutions. Design Python and SQL pipelines, warehouse and lakehouse models, document ingestion and retrieval-ready data foundations. Implement data quality, lineage, access controls, testing, deployment, monitoring, and recovery processes. Mentor engineers, manage technical delivery, collaborate with clients and vendors, and contribute to reusable assets, proposals, and implementation roadmaps.

Develops and leads data engineering solutions for web applications supporting intelligence-community data custodians. Responsibilities include mapping and modeling data transformations, documenting data flows, ingesting datasets into corporate repositories, integrating vendor products, deploying clustered or cloud storage, and designing secure, scalable systems. The role requires extensive systems engineering leadership, advanced data integration expertise, and an active TS/SCI clearance with current polygraph.

Build and maintain scalable data pipelines from financial data vendors into Snowflake and Databricks gold tables supporting machine learning. Lead end-to-end projects involving LLMs, RAG, and AI technologies; implement CI/CD, infrastructure-as-code, containerization, testing, monitoring, and cloud-native deployments. Collaborate with stakeholders, ensure data quality, mentor junior engineers, and travel up to 20% to support distributed teams.

Designs and manages advanced data architectures, pipelines, ingestion frameworks, and data quality processes. Develops scalable data solutions across cloud, warehouse, lakehouse, and distributed computing platforms. Builds data consumption methods such as APIs, database views, and extracts while managing lineage, privacy, compliance, and transformation rules. Collaborates cross-functionally to improve data sourcing, accessibility, reliability, and processing efficiency.

Develop and maintain data pipelines for GEOINT datasets, transform and standardize data for machine learning environments, assist with data service development, document workflows, analyze data, and support data quality. Collaborate with senior engineers and scientists on Department of War artificial intelligence initiatives while communicating findings and adapting to changing priorities.

Senior Data Engineer responsible for monitoring enterprise data pipelines, resolving production incidents, implementing data quality checks, automating operational workflows, and improving reliability across pharmacy data environments. The role also involves AI-enabled process optimization, data governance, documentation, technology evaluation, and operational best practices. Required experience includes data engineering, Azure cloud platforms, Scala or PySpark, SQL, Databricks, Airflow, Azure Data Factory, and production pipeline support.

Build and own scalable finance and revenue data pipelines, models, and datasets supporting ARR, NRR, bookings, billing, and revenue attribution. Ensure data quality, reliability, monitoring, reconciliation, and self-service access while partnering with Finance, Revenue Operations, Analytics, Product, and Engineering.

Designs and maintains secure, scalable Microsoft Fabric data pipelines, Lakehouses, warehouses, and infrastructure. Implements encryption, access controls, governance, compliance, monitoring, CI/CD, infrastructure-as-code, containerization, and automated security testing. Collaborates with security, platform, data science, and engineering teams to improve data reliability and operational excellence. Requires strong SQL, data engineering, Azure, DevOps, and data security experience, along with US citizenship.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Designs, builds, and operates scalable cloud data platforms, pipelines, and applications using Python, SQL, Spark, AWS, Databricks, Snowflake, and distributed data technologies. The role includes data modeling, real-time processing, data security, observability, architecture standards, stakeholder communication, mentoring, and collaboration with product, engineering, analytics, and machine learning teams.

Designs, develops, and maintains Azure-based data platforms, ETL/ELT pipelines, data warehouses, data lakes, and database systems. Manages Azure services, networking, security, monitoring, automation, performance tuning, backup and recovery, high availability, and disaster recovery. Supports enterprise reporting, analytics, Power BI environments, production incidents, migrations from on-premises systems, and collaboration with analysts, DBAs, developers, and infrastructure teams.

Design and build scalable data infrastructure, including lakehouse architecture, ClickHouse deployments, ETL/ELT pipelines, and data models connecting Postgres with analytics systems. Establish data quality, observability, performance, cost, and reliability standards. Collaborate with product and engineering teams, define data contracts and specifications, support real-time analytics, and use AI-assisted coding and LLMs to accelerate pipeline development and transformation.

Own and evolve the Azure-based data platform for a large-scale telematics SaaS platform. Build high-throughput IoT ingestion, transformation, enrichment, storage, and analytics pipelines using Cosmos DB, Azure Data Explorer, Azure SQL, Fabric, Event Hubs, and Functions. Design scalable telemetry and multi-tenant data models, ensure pipeline reliability and observability, optimize costs and performance, implement governance and CI/CD, and collaborate with engineering, architecture, product, and leadership teams.

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Collaborates with data scientists, engineers, and mission stakeholders while applying data validation, modeling, quality, testing, documentation, and Agile practices. The role is onsite in a SCIF at Offutt AFB and supports classified defense missions.

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Applies data validation, modeling, and quality practices while collaborating with data scientists, engineers, and mission stakeholders. The role requires onsite work in a SCIF and an active U.S. government security clearance.

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Collaborates with data scientists, engineers, and mission stakeholders while applying data validation, modeling, testing, documentation, and Agile practices. The role requires onsite work in a SCIF and an active U.S. government security clearance.

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Collaborates with data scientists, engineers, and mission stakeholders while applying data validation, modeling, quality, testing, documentation, and version-control practices. The role is onsite in a SCIF at MacDill AFB and requires an active Secret clearance, with potential for higher-level clearances.

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Collaborates with data scientists, engineers, and mission stakeholders while applying data validation, modeling, testing, documentation, and Agile practices. This is an onsite SCIF role supporting classified national security missions.

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Collaborates with data scientists, engineers, and mission stakeholders while applying data validation, modeling, quality, testing, documentation, and Agile practices. The role requires onsite work in a SCIF and an active Secret clearance, with potential TS/SCI requirements.

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Applies data validation, modeling, quality practices, testing, version control, and documentation while collaborating with technical, nontechnical, and mission stakeholders. The role is onsite in a SCIF and supports classified government missions.

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Collaborates with data scientists, engineers, and mission stakeholders while applying data validation, modeling, quality, testing, documentation, and Agile practices. The role requires onsite work in a SCIF and support for classified defense environments.

Develops and maintains secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets. Processes structured and unstructured data using platforms such as Databricks, Spark, Palantir Foundry, and Maven Smart Systems. Partners with data scientists, engineers, and mission stakeholders while applying data validation, modeling, quality, testing, documentation, and Agile practices. The role is onsite in a SCIF and supports classified defense missions.

Build and optimize large-scale data pipelines and products using Databricks, Apache Spark, Java, and related technologies. Ingest, transform, validate, and prepare data for analytics and machine learning. Work with distributed systems, data lakes, Parquet, and Delta Lake; troubleshoot data quality and model issues in secure environments. Collaborate with engineers, analysts, customers, and stakeholders to deliver production-quality solutions and actionable insights.

Develop and automate data workflows, analytical tools, visualizations, and AI/ML capabilities supporting engineering, product design, sales, and manufacturing. Translate business requirements into automated solutions, monitor production datasets, improve analytics workflows, collaborate with IT and cross-functional teams, and communicate analytics value to stakeholders. The role also provides data expertise and documentation across the organization.

Build and operate batch and real-time data pipelines and production data products for an enterprise customer. Integrate databases, APIs, files, and event streams while ensuring data quality, observability, lineage, and reliability. Act as the primary technical customer partner, translating needs into data models, APIs, and delivery plans. Coordinate releases, manage technical projects, resolve production issues, document operations, and provide field feedback to improve the platform.