Top Data & Analytics Jobs

Design, build, optimize, and maintain scalable batch and near-real-time data pipelines across on-premises and cloud platforms. Develop Snowflake data models, manage enterprise data integration and governance, implement data quality controls, and support reporting and analytics. Collaborate with data scientists, analysts, infrastructure, DevOps, privacy, and compliance teams. Troubleshoot pipeline issues, monitor performance, participate in architecture and Agile development activities, and mentor associate data engineers.

Designs, builds, and maintains scalable data pipelines, data marts, enterprise data platforms, and ETL processes. Integrates diverse data sources, improves data quality and performance, and supports secure analytics infrastructure using Microsoft Fabric and Azure. Develops Tableau and Power BI dashboards, collaborates with cybersecurity and cross-functional teams, manages project deliverables, documents processes, and trains team members on data organization and interpretation.

Designs and deploys scalable data pipelines, preprocessing workflows, feature-engineering services, and machine learning models for secure Department of Defense environments. Develops reproducible, tested, containerized data and ML services using Python, Spark, Databricks, Docker, Terraform, SageMaker, and MLflow. Performs exploratory analysis, visualization, data storytelling, model evaluation, and decision-support development while collaborating with product, software, platform, DevSecOps, data science, and mission teams.

Participate in an internship focused on database modeling and optimization, data analysis and consolidation, data quality, and data support. Work alongside experienced professionals on real-world projects while developing technical and professional skills. The program requires prior knowledge of data concepts, spreadsheets, databases, programming logic, and analytical problem-solving.

Designs and maintains cloud-based data pipelines and architectures, primarily using Snowflake. Builds ETL/ELT processes, automates reporting, supports predictive analytics and machine learning initiatives, and helps migrate legacy platforms to the cloud. The role involves data modeling, governance, troubleshooting, AI solution development, and collaboration with analysts, data scientists, actuaries, product managers, and insurance professionals. The position is onsite four days weekly with one remote day.

Build and troubleshoot data ingestion, transformation, ETL/ELT, and integration pipelines; administer data management platforms; implement data quality, governance, metadata, lineage, and lifecycle processes; automate workflows; support secure data systems; and work directly with government mission users to onboard datasets, resolve issues, and enable analytical and AI applications.

Build and maintain data environments in AWS and Snowflake by identifying, preparing, manipulating, and modeling structured and unstructured datasets. Evaluate datasets for reporting and modeling, use data preparation tools such as Coalesce, resolve data quality issues, and advise stakeholders on data systems and concepts.

Lead Data Engineer responsible for designing, developing, testing, and supporting cloud-based data solutions. Collaborate across Agile teams to build distributed data systems, work with streaming and batch technologies, mentor engineers, and ensure code quality and performance for large-scale data platforms.

Lead enterprise data architecture and platform design across cloud, hybrid, and on‑prem environments. Own data modeling, database architecture, lake house/warehouse implementations, ETL/ELT pipelines, data governance, security, observability, and DevOps automation. Mentor teams, align platforms to business goals and regulatory needs, and drive modernization and performance optimization.

Design, build, and operate end-to-end ETL/ELT pipelines to ingest network telemetry into a Data Mart. Create high-performance dimensional models, implement automated data quality and observability, enforce governance and IAM, maintain metadata and lineage, and enable analysts with production-ready datasets and standardized metrics.

Design, develop, and maintain SSRS reports and Power BI dashboards; perform data modeling and DAX calculations; support ETL (SSIS) and validate data across EMR/billing/CRM systems; gather and translate stakeholder requirements; liaise with offshore teams; perform testing, documentation, and ensure HIPAA/PHI compliance and data governance.

Design, build, and maintain scalable Spark/Python data pipelines and automated workflows. Enforce data governance, quality, lineage, and observability. Support ML pipelines (feature engineering, validation, monitoring) and document data assets, contracts, and standards.

Lead technical strategy, architecture, and modernization of enterprise master data and data lakehouse platforms. Design and implement large-scale data pipelines, databases, and distributed systems using Python, Spark, Redshift, Iceberg and AWS services. Ensure performance, reliability, governance, CI/CD, and AI-enabled engineering practices while mentoring senior engineers and aligning cross-functional stakeholders.

Builds and operates scalable batch and real-time data pipelines (Databricks/Spark, Kafka), maintains Customer 360 data model and feature pipelines, ensures data quality/governance, delivers governed customer attributes to activation platforms, and supports privacy/compliance.

Build and maintain scalable batch and real-time data pipelines, model data to power analytics and ML, and operate distributed data systems. Collaborate with business, engineering, and data science teams to ensure data quality, documentation, and debugging while adopting new technologies and working in an ambiguous, fast-changing environment.

Build and maintain low-latency, highly available distributed data systems and services to deliver high-quality data for product, analytics, and ML. Partner with data scientists and product teams to design scalable, observable, multi-tenant data products using batch and real-time processing frameworks. Ensure 24x7 reliability, monitoring, and operational excellence while owning end-to-end delivery.

Design, build, and own large-scale batch and streaming data pipelines and core Ads data products. Collaborate with analytics, data science, and engineering teams to model, transform, govern, and deliver high-quality ad datasets for analysis, ML, and reporting while ensuring privacy compliance.

Build scalable, privacy-focused data frameworks and ETL pipelines using big-data technologies (Spark, Presto, Flink, Kafka, Hive). Ensure appropriate handling of personal data, support privacy and legal needs, partner cross-functionally, and lead system design while mentoring teammates.

Build and scale robust production data pipelines and automate complex game-related datasets, ensure high data quality, design schemas and data models, collaborate with logging infrastructure and analytics stakeholders to enable analysis across Netflix's games library.

Support end-to-end data engineering: gather requirements, build and monitor ETL pipelines, implement data quality, contribute to data models, run profiling and basic experiments, document solutions, and communicate progress with stakeholders.

Lead Data Engineer responsible for designing, developing, testing, and supporting cloud-based data publication and transformation solutions. Collaborate across Agile teams to build distributed, real-time data pipelines and data warehousing (Redshift/Snowflake), perform unit tests and code reviews, mentor engineers, and adopt emerging technologies to deliver scalable data platforms.

Build and own the unified data layer: ingestion pipelines into ClickHouse, data modeling and entity resolution, canonical metric definitions, quality checks, and internal data tools. Support ad-hoc analyses and work closely with engineering to ensure stable, trustworthy data powering customer dashboards and product BI.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Design, build, and operate scalable cloud data pipelines and platforms (Azure preferred). Implement DevSecOps, CI/CD, automated testing, monitoring, performance tuning, and production readiness. Mentor engineers, drive operational excellence, and translate requirements into robust technical designs. Apply healthcare domain knowledge and collaborate in agile teams to deliver secure, reliable data products.

Lead design and implementation of scalable cloud data lakes, warehouses, and marts. Build ELT/ETL pipelines, apply Data Vault 2.0 and dimensional modeling, implement data quality, lineage, observability, and governance. Optimize Snowflake/Databricks/Synapse platforms, champion CI/CD and DataOps, mentor engineers, and enable AI/ML-ready data products through collaboration with architecture and product teams.