Top Data & Analytics Jobs

Develop and support enterprise data pipelines, integrations, data models, ETL/ELT processes, quality checks, and analytics environments. Build Power BI datasets, semantic models, dashboards, and reports for Finance, Sales, Operations, and Supply Chain. Troubleshoot data, pipeline, reporting, and performance issues; improve governance and reliability; and help transition Microsoft Fabric ownership from external partners. Partner with technical and business stakeholders to deliver scalable data solutions and support future AI and automation capabilities.

Designs and leads enterprise data pipelines and integrations across Snowflake, Oracle Fusion, on-premises databases, and cloud applications. Ensures data quality, governance, security, scalability, and performance while supporting Power BI, AI/ML, and self-service analytics. Partners with business and technical stakeholders, leads migrations, mentors engineers, optimizes data platforms, and establishes engineering best practices.

Design and operate end-to-end infrastructure for intracortical neural data, from ingestion and validation through preprocessing, versioned dataset releases, and training data loaders. Preserve scientific metadata and provenance while implementing signal processing, quality checks, storage optimization, parallel processing, and monitoring. Collaborate with neuroscience, machine learning, infrastructure, and IT teams to support scalable, reliable model training and sensitive research-data management.

Lead end-to-end data engineering workstreams, building reliable cloud pipelines, governed AI and retrieval workflows, trusted data models, event-driven systems, and automated recovery. Own production reliability, security, observability, cost optimization, testing, audit evidence, and operational readiness. Use Python, advanced SQL, coding agents, machine learning, and defensive AI engineering to deliver secure, resilient platforms while mentoring engineers and translating business needs into practical solutions.

Designs, builds, deploys, and operates scalable data pipelines, datalake systems, streaming and batch processing frameworks, MLOps infrastructure, data documentation tooling, and an agentic data exploration platform. The role improves engineering velocity, data reliability, observability, discoverability, and machine learning development across customer-facing products.

Designs, builds, deploys, and maintains scalable data pipelines, data lake systems, batch and streaming processing, MLOps frameworks, data documentation tooling, and an agentic data exploration platform. The role supports customer-facing products and enables engineering teams through reliable infrastructure, observability, data discovery, and AI capabilities.

Develop and maintain scalable ETL/ELT pipelines and distributed data processing solutions using Python, PySpark, and AWS. Build cloud data solutions with S3, EMR, and Lambda; automate infrastructure with Terraform; integrate REST APIs, Kafka, and MQ; support Cassandra storage; orchestrate workflows with Airflow and Control-M; and troubleshoot performance, reliability, and data quality issues.

Build and operate scalable batch and streaming data pipelines, data lake infrastructure, MLOps frameworks, data documentation systems, and an AI-assisted data exploration tool. The role focuses on reliable data ingestion, large-scale processing, workflow orchestration, observability, and infrastructure supporting customer-facing products and machine learning development.

The Senior Data Engineer designs, develops, and maintains scalable data pipelines and infrastructure using AWS Glue, Kafka, and Step Functions. Responsibilities include ETL development, data modeling, data quality, troubleshooting, compliance, and collaboration with cross-functional teams. The role also mentors Data Engineers and supports analytics and business intelligence initiatives. Required expertise includes SQL, Python, AWS services, data warehousing, and ETL tools such as Informatica, Talend, or SSIS.

Designs and operates billion-scale data platforms supporting streaming, batch, advertising, marketing, measurement, and machine learning workloads. Builds reliable, tenant-aware pipelines with replayability, fault recovery, observability, quality controls, and cost efficiency. Leads domain architecture, performance optimization, design reviews, mentoring, incident response, and governed automation across global advertising products.

Designs and deploys scalable data solutions supporting AI agents, LLM workflows, and autonomous decision-making. Builds cloud-based pipelines, semantic layers, data models, monitoring, governance, and full-stack services. Partners with engineering, AI/ML, product, and business teams to deliver reliable, traceable, high-performing data products and enterprise applications. Requires expertise in distributed data processing, cloud platforms, databases, APIs, DevOps, and modern frontend and backend development.

Design, build, validate, and support reliable data solutions for enterprise operations, analytics, and decision-making. Responsibilities include developing pipelines, transformations, and models; translating business requirements; implementing data quality, security, monitoring, and recovery controls; optimizing performance and cost; documenting lineage and procedures; and collaborating with stakeholders throughout delivery. The role requires strong SQL, Python or Scala, ETL/ELT, data modeling, Spark or equivalent processing, orchestration, and cloud data platform experience.

Build and maintain production data ingestion, transformation, and pipeline systems for a governed federal data and AI platform. Develop data products, infrastructure-as-code, CI/CD workflows, monitoring, quality controls, cataloging, and access controls within FedRAMP, NIST 800-171, and CUI constraints. Support data science and machine learning teams, troubleshoot reliability and performance issues, and document engineering processes. The role is fully remote and requires U.S. citizenship and the ability to obtain a T5/SSBI adjudicated clearance.

Designs, builds, validates, and supports enterprise data solutions, including pipelines, transformations, models, integrations, and platform components. Implements data quality, security, access controls, monitoring, and recovery procedures; optimizes performance and cost; documents definitions, lineage, and operational processes; and translates business requirements into reliable technical solutions. Collaborates with stakeholders, supports production delivery, and may mentor peers and review technical work.

Build and maintain production data pipelines that transform robot logs and telemetry into reliable, queryable datasets. Design Spark-based ETL workflows, orchestrate processing with Airflow or similar tools, manage gold-layer datasets, and implement data quality, schema evolution, monitoring, and metadata practices. Collaborate with robotics, hardware, reliability, analytics, and ML teams on data modeling, fleet insights, debugging, and root-cause analysis. The role may also contribute to Python, Java, or C++ libraries supporting data capture and consumption.

Design and build petabyte-scale data pipelines for genomic and imaging data, transforming public and proprietary datasets into AI-ready training data. Develop agent-based curation and quality-control systems, data cataloging tools, and reliable observable infrastructure. Collaborate with AI researchers and scientists to define data requirements and integrate diverse biological datasets while addressing performance, bandwidth, scalability, and scientific correctness.

Develop and lead operational analytics, monitoring, metrics, and automation initiatives for enterprise applications and infrastructure. Build Splunk dashboards, reports, alerts, and advanced SPL queries; analyze logs, APIs, errors, and operational trends; establish KPIs; and translate findings into actionable recommendations. Create automation using Python, Shell/Bash, and Java to streamline reporting, troubleshooting, and data processing. Collaborate with development, infrastructure, operations, and business teams while improving reliability, monitoring coverage, and incident response.

Designs, builds, and supports a near-real-time transactional cache platform using CDC replication from UniVerse to PostgreSQL, transformation pipelines on GCP, and optimized MongoDB Atlas models. Responsibilities include data modeling, indexing, query and performance optimization, production ETL/ELT support, monitoring, incident response, reliability improvements, documentation, and collaboration with API and application teams. The role also mentors junior engineers and contributes to scalable cloud-native data platform engineering practices.

Designs and maintains production data pipelines for AI usage and cloud billing data, normalizing costs for accurate attribution and chargeback. Builds Power BI dashboards, automates recurring financial reporting, implements quality controls and anomaly detection, and ensures reliable, auditable cost data. Partners with Finance and FinOps stakeholders to evolve data models, tagging standards, allocation logic, and reporting as AI consumption grows.

Leads the design, development, and operation of production-grade data pipelines and governed data products supporting operational resiliency and recovery-risk analytics. Responsibilities include data modeling, ETL, database optimization, data quality and lineage, cloud and event-driven architectures, automation, testing, and CI/CD. Partners with cybersecurity, technology controls, engineers, architects, analysts, and business stakeholders to translate risk and control requirements into scalable, auditable solutions.

Develop and optimize SAP Data Services ETL/ELT workflows integrating SAP and non-SAP systems. Create source-to-target mappings, extraction routines, transformations, data quality processes, and profiling solutions. Monitor and troubleshoot jobs, tune SQL and database performance, and apply SAP HANA optimization techniques. The role requires collaboration across functional and technical teams while supporting a mission-critical Department of Defense transportation and logistics environment.

Designs, develops, deploys, and supports scalable batch and streaming data pipelines, lakehouse solutions, warehouses, and governed data products. The role uses AWS, Spark, orchestration, data modeling, quality controls, security, DevSecOps, and data mesh practices to support analytics, BI, data science, and AI. It also provides technical leadership, manages risks and dependencies, partners with business and technology teams, and owns solutions through production operations and continuous improvement.

Designs and leads production-grade batch, incremental, streaming, and real-time data pipelines across Microsoft Fabric and Azure. Builds Delta Lake medallion architectures, dimensional and Data Vault models, CDC and incremental-loading solutions, CI/CD automation, governance, security, observability, and data-quality controls. Supports migrations to Fabric, prepares governed data for AI and RAG workloads, and leads client-facing engineering workstreams, estimates, risk management, mentoring, and operational handover.

Own PushPress’s data platform, including ingestion, orchestration, cloud warehouse performance, governance, privacy, data quality, monitoring, and AI-ready data foundations. Build reliable pipelines integrating product, payment, and third-party data; establish data contracts, access controls, auditability, and semantic definitions; support real-time analytics and trustworthy reporting. Partner with Engineering, Data, and AI teams while maintaining infrastructure, incident response, documentation, and operational runbooks.

Build governed data models, tested analytics transformation layers, ETL pipelines, semantic datasets, and AI-ready data workflows. Partner with analysts, developers, and domain experts to define metrics, schemas, documentation, lineage, and validation frameworks. Support ML features, retrieval pipelines, unstructured data, and LLM output benchmarking while applying software engineering, compliance, and performance standards.