Top Data & Analytics Jobs

The Senior Data Engineer will design and manage petabyte-scale data pipelines, ensuring data quality and architecture for operational decision-making and ML training.

The Principal Database Architect & Engineer will own the data components of complex infrastructure solutions and lead teams to deliver innovative database technology solutions, ensuring optimal performance and operational efficiency.

The Senior Data Engineer will manage data tuning on relational databases, utilize Big Data technologies, and coordinate with technical teams and business users.

The Senior Data Engineer will design and manage data pipelines for massive datasets, ensuring quality, cost management, and effective data operations. Responsibilities include overseeing data flows, monitoring data quality, and managing vendor partnerships.

The Senior Data Engineer will optimize data architecture, troubleshoot integrations, and refactor legacy systems within a product team focused on client applications.

Design, build, and operate large-scale AWS-based ETL pipelines using Glue/PySpark to ingest, transform, and curate multi-format data into Apache Iceberg tables. Ensure data quality, metadata management, semantic layer creation (Trino/Athena), CloudFormation deployments, documentation, and compliance within a FedRAMP federal agile environment.

Own and improve the company data stack: expand the data warehouse, build and streamline Python-based ingestion and ETL processes, refactor dbt models, maintain data quality, standardize reporting, and collaborate with engineering and analysts. Act as technical owner for parts of the stack and proactively drive improvements and automation.

Design, build, and maintain ETL/ELT data pipelines and data models from SQL Server, PostgreSQL, SaaS, and internal apps. Ensure data quality, monitor ingestion, optimize Snowflake performance, collaborate with stakeholders to deliver dashboards and analytics, and recommend data architecture improvements.

Design, build, and iterate data engineering pipelines, dashboards, and reports to support analytics and data science. Partner with stakeholders to translate requirements into engineering solutions, promote self‑service analytics, define best practices, and support implementation using Databricks, Snowflake, and Azure components.

Lead the analytics data layer: design and operate a ClickHouse-backed warehouse, define and validate trusted metrics, build customer-facing React dashboards and internal Grafana/self-serve tooling, and ensure data quality, lineage, and documentation for telemetry-derived insights.

Serve as a data quality and QA engineer on a federal data platform: build automated AWS Glue/Deequ data quality checks, validate schemas and ETL pipelines, create and run unit/integration tests with 90% coverage, support IV&V and UAT, perform static/security scans, produce test documentation, monitor production ETL, and collaborate with data stewards and IV&V teams.

Own and build the end-to-end data layer: design high-throughput real-time ingestion, architect petabyte-scale open table formats, implement and optimize Spark-based batch/stream pipelines, enable Kafka/Flink integrations, ensure performance/reliability/cost efficiency, define data contracts and schemas, and establish infrastructure best practices on AWS GovCloud, Kubernetes, and Airflow.

Lead and architect large-scale data products and pipelines across teams. Design and deliver ELT/ETL solutions, evaluate tools, ensure scalability and integrity, influence technology decisions, and research emerging big-data/cloud methodologies (AWS, Hadoop/EMR, Spark, Kafka, Snowflake). Partner with architects and stakeholders to drive implementation and operationalize production workloads.

Design, build, and maintain data ingestion, ETL, storage, and analytics systems to measure and scale Starlink network performance (including Wi‑Fi). Automate metric aggregation, detect widespread application issues, lead investigations, fuse multi-source data, and apply analytics and ML models to vehicle- and ground-generated data.

Build and maintain reliable Apache Airflow pipelines and workflow engines to move, schedule, and monitor data. Develop automation using Bash, Python, and Java. Work with distributed processing (Spark), containerization (Docker/containerd/Podman), and Git source control to support analytics and operational needs.

Design, build, and maintain interactive Power BI dashboards (including Fabric workspaces). Analyze data to identify trends, ensure data quality, collaborate with stakeholders to gather requirements, and present actionable insights to support decision-making.

Lead data engineering projects across cross-functional teams. Design resilient batch and real-time analytics systems, optimize pipelines and infrastructure, integrate ML models, and provide technical leadership, coaching, and mentoring to improve reliability, scalability, cost-efficiency, and maintainability.

Design, build, and maintain large-scale data pipelines and APIs for Ventura OS. Optimize data processing workflows, integrate ML models into production, ensure data quality and observability, and collaborate across teams to drive architecture and data infrastructure decisions for streaming and content recommendation use cases.

Define and lead the technical architecture for a lakehouse on AWS, build production pipelines and data models for time-series and multi-source data, introduce data governance and catalog foundations, author platform playbooks (ADRs, runbooks, Terraform modules), deliver projects end-to-end, and mentor engineers while representing the platform to senior leadership and non-technical stakeholders.

Design, develop, QA, and maintain BI dashboards using Qlik to transform structured and unstructured data into actionable visualizations. Partner with clients to explore, clean, analyze data, build user-centric UI elements, and communicate insights to technical and non-technical stakeholders.

Lead design and implementation of the enterprise data platform: architect scalable warehouse/lakehouse solutions, build and optimize ETL/ELT pipelines, enforce data quality and governance, tune analytical workloads, and enable reporting and self-service analytics in support of BI and AI initiatives.

Maintain and evolve the data ecosystem for fraud detection and analytics: build and operate ELT/ETL pipelines, integrate multiple data sources, implement monitoring/alerting, automate processes, collaborate with data scientists and commercial teams, mentor junior engineers, and communicate technical work to non-technical stakeholders.

Design and operate real-time and batch data pipelines on Azure and Databricks for IoT/telemetry. Build ETL/ELT workflows, streaming ingestion, data services/APIs, and ensure data quality, privacy, performance tuning, and CI/CD for analytics and ML consumption.

Designs and develops cloud-native ETL/ELT solutions and data warehouses using Informatica, Python, Oracle/PLSQL and scheduling tools. Architects data pipelines, optimizes SQL performance, automates jobs, supports testing and implementation, and documents applications across multiple projects.

Design, build, and operate petabyte-scale data infrastructure: real-time ingestion, storage (open table formats), and serving. Architect scalable Iceberg-based data lakes, optimize Spark pipelines and streaming (Kafka/Flink), drive reliability, cost efficiency, and data contracts; collaborate with product and platform teams and establish best practices and tooling.