Top Data & Analytics Jobs

Build and operate production-grade data pipelines and products for manufacturing customers. Lead discovery, architecture, implementation, testing, deployment, documentation, and operational handoff. Model manufacturing data, ensure data quality and observability, and create reusable pipelines and analytics products. Work directly with customer data, manufacturing, IT, product, and engineering teams to solve ambiguous operational problems and deliver reliable solutions.

Provides data engineering and analysis consulting for a Department of Defense client. Responsibilities include data modeling, data integration, software development, collaboration with clients and technical teams, and delivery of data solutions. The role requires an active Secret clearance, a bachelor’s degree, and at least three years of relevant experience. Preferred qualifications include Palantir, visualization, big data, programming, ETL, consulting, and Agile experience.

Designs, builds, and maintains data infrastructure, ETL pipelines, data models, and analytics data warehouses. Ensures accurate data flow from creation through presentation layers and supports reporting, analytics, and machine learning initiatives. Collaborates with stakeholders to deliver data products, improves engineering tooling and pipelines, participates in on-call support, and uses cloud, containerization, orchestration, streaming, and CI/CD technologies.

Leads the development of customer-focused data and analytics solutions, including data models, reports, dashboards, and self-service tools. Partners with stakeholders and data product owners to improve data quality, governance, accessibility, and analytics adoption. Provides technical leadership, mentors team members, manages project deliverables, and supports scalable data products. Uses SQL, Python, visualization platforms, and other analytics tools to transform data and deliver actionable business insights.

Design and lead development of TrendAI’s large-scale data platform, including ingestion, CDC, replication, transformation, governance, cataloging, semantic discovery, and analytics capabilities. Enable governed self-service dbt across OLTP systems, build distributed SQL and streaming architectures, establish data quality and lineage standards, and create reusable platform APIs. Provide technical leadership, mentor engineers, and guide cross-team architecture for a security-focused, multi-product data platform supporting analytics and AI applications.

Leads the design, development, and optimization of scalable batch and streaming data pipelines for drilling, equipment, and operational analytics. Builds data models and features supporting reporting, machine learning, AI, predictive maintenance, and drilling optimization. Ensures data quality, testing, monitoring, observability, and CI/CD reliability while providing technical leadership, mentoring, and cross-functional collaboration with engineering, data science, and operations teams.

Own the technical direction and roadmap for Faire’s data infrastructure. Build CDC and streaming ingestion from production databases into Kafka, Iceberg, and S3; manage data quality, contracts, observability, SLAs, and reliability. Operate Airflow and Fivetran, lead platform migrations, establish governance, mentor engineers, and collaborate with data scientists, analysts, and senior engineering teams. The role requires hands-on development, architecture leadership, incident ownership, and cross-team influence.

Owns the analytics layer between curated enterprise data and business users. Builds governed Gold-layer data products, dimensional models, Power BI semantic models, standardized metrics, and reusable data assets. Implements Unity Catalog governance, metadata, lineage, security, quality monitoring, reconciliation, and deployment controls. Partners with analysts, business owners, IT, data stewards, and AI engineers to deliver trusted, documented data for self-service analytics and AI consumption.

Design, build, and operate Azure-based data pipelines and integrations. Consolidate data from multiple sources into centralized SQL systems, perform transformations and field mapping, and maintain data warehouse capabilities. Troubleshoot quality, integrity, latency, security, and reliability issues while applying governance, monitoring, and SLA practices. Collaborate with engineers, analysts, and stakeholders in Agile pods to deliver reusable, analytics-ready data assets.

Lead the architecture and delivery of end-to-end Azure data platforms, including ingestion, transformation, modeling, analytics, activation, APIs, and agentic AI integrations. Build production-grade batch and streaming pipelines using Microsoft Fabric, Azure Databricks, PySpark, Python, and SQL. Establish governance, security, lineage, and performance standards; troubleshoot critical systems; mentor engineers; advise client stakeholders; contribute to presales and reusable engineering frameworks.

Build and maintain Azure-based ETL and ELT pipelines supporting a layered cloud data lakehouse. Integrate ERP and operational data through REST APIs and SQL sources, implement incremental loading and schema evolution, manage orchestration workflows, and monitor data quality and pipeline health. The role also includes data modeling, documentation, testing, deployment practices, code reviews, and collaboration with leadership while developing toward broader engineering ownership.

Design and build a durable data storage platform that preserves source disagreements, provenance, uncertainty, historical versions, and evidence. Develop result and evidence stores, lineage tracking, versioned rules and configurations, reproducible decision records, and point-in-time entity views. Collaborate with entity-resolution and machine-learning teams to support probabilistic outputs. The role requires production data engineering experience, strong Python and SQL, PostgreSQL expertise, U.S. citizenship, and an active Secret clearance.

Designs, develops, tests, deploys, and supports enterprise data products, pipelines, transformations, and analytics solutions using Databricks and cloud technologies. Builds governed Bronze, Silver, and Gold lakehouse assets; implements data quality, monitoring, security, and access controls; and develops reusable data models and engineering components. Collaborates with architects and Agile delivery teams, participates in reviews and CI/CD, troubleshoots performance issues, automates operational processes, and documents technical solutions. The role supports reporting, operational workloads, and AI/ML use cases.

Own Synera’s data warehouse and ingestion infrastructure, integrating GTM and business systems into reliable, documented, monitored pipelines. Build data models, reporting and forecasting layers, and agent-ready interfaces using APIs, MCP connectors, permissions, and governance. Develop retrieval infrastructure for documents and unstructured content, including chunking, embeddings, and indexing. Partner with teams across the company to improve data quality and make business information usable by AI agents.

Designs and implements scalable software solutions, develops maintainable features, resolves bugs, optimizes performance, and writes automated tests. Participates in architecture discussions, code reviews, Agile planning, CI/CD, observability, and adoption of AI-assisted engineering tools. Collaborates with QA, Product Management, and DevOps to deliver software across web, desktop, cloud, and IoT environments. The role is remote for candidates in the Seattle or Los Angeles metropolitan areas, with quarterly travel to Los Angeles.

Design, build, and maintain scalable data platforms and ETL/ELT pipelines using Databricks/Spark and AWS. Develop backend data services in C#/.NET, ensure data quality and performance, leverage AI-assisted coding tools, collaborate with cross-functional teams, lead technical design decisions, and mentor engineers.

Develop and optimize large-scale data systems, ETL pipelines, storage, entity-resolution products, and data automation infrastructure. Build and integrate ingestion and processing pipelines using AWS, Airflow, Spark, PySpark, and EMR. Create testing and monitoring components, develop analytical tools, support data governance and quality, collaborate with data science teams, and document technical solutions.

Develop and maintain interactive BI dashboards, reports, data models, and pipelines using Power BI, Tableau, and Amazon QuickSight. Translate stakeholder requirements into visualization solutions, perform ETL and administrative tasks, manage data connections and governance, and provide user support and training. Create documentation and collaborate with federal stakeholders to deliver real-time organizational insights.

Design, build, deploy, and support production data pipelines and enterprise data solutions in Databricks and GCP. Develop data models and layered architectures, ensure data quality, security, and governance, integrate multiple sources, and support analytics, AI/ML, reporting, and operational decision-making while collaborating with technical teams and federal stakeholders.

Develops advanced data engineering and visual analytics applications for structured and semi-structured data. Designs enterprise database systems, builds large relational databases, develops and optimizes data pipelines, integrates systems with existing data warehouses, and improves system performance. Requires Java, AWS, ETL, Spark, Scala, SQL, PostgreSQL, NiFi, Informatica, and Linux experience, along with an active Top Secret/SCI-eligible clearance.

Build and oversee reliable ETL/ELT pipelines and data products that transform complex customer data into trusted analyses, visualizations, quality reports, and AI-powered insights. Responsibilities include data exploration, quality control, anomaly detection, AI/ML output evaluation, customer collaboration, documentation, and improving data-cleaning and analytics tools. The role requires cross-functional work in an Agile environment focused on security, data integrity, and clear communication.

Architect and maintain secure, scalable data platform infrastructure and resilient batch and streaming pipelines. Build infrastructure using Terraform and Helm, deploy cloud data services, optimize schemas and database performance, and implement monitoring and alerting. Partner with application teams to resolve data issues, establish governance practices, and support MLOps through model versioning, containerization, and deployment.

Build and maintain ETL/ELT pipelines, explore and clean complex customer data, and deliver trusted analytics products, reports, visualizations, and AI-powered data capabilities. Partner with customers and internal teams to define data requirements, investigate discrepancies, validate AI/ML outputs, implement quality controls, and improve data tooling through automation. Translate complex findings and workflows into clear narratives and repeatable processes.

Architect and maintain secure cloud data infrastructure, scalable schemas, and resilient batch and streaming pipelines. Deploy data platforms using Terraform, Helm, CI/CD, and infrastructure-as-code practices. Build monitoring and alerting for data health, system performance, and container observability. Troubleshoot application workflows, database queries, and pipeline logic while advancing data governance. Support MLOps through AI model versioning, containerization, and deployment.

Leads the design, administration, optimization, and modernization of enterprise data platforms across SQL Server, Azure, and Microsoft Fabric. Responsibilities include database operations, high availability and disaster recovery, performance tuning, replication, cloud migrations, monitoring, security, governance, and automation. The role collaborates with architects and infrastructure teams, mentors junior engineers, and serves as a subject matter expert for database infrastructure and cloud architecture.