Top Data Engineer Jobs

The Senior Data Engineer will work with data tuning, big data technologies, and coordinate with technical teams and business users.

The Senior Data Engineer will work with data tuning on relational databases, utilize big data technologies, and coordinate with teams. A strong academic background and experience with large datasets are required.

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

The Senior Data Engineer will design and manage data pipelines, work with complex datasets, and collaborate with teams to enhance data product integrity and effectiveness.

As a Senior Data Engineer, you will build and maintain data pipelines, support analytics needs, and ensure data integrity for operations and reporting at Finix.

Design, build, and maintain Snowflake data models, warehouses, and ELT/ETL pipelines; develop backend services in Python and C#; manage CI/CD and deployments; troubleshoot production issues; collaborate with clients and mentor junior engineers.

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.

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.

Design and develop scalable data and analytics solutions on cloud and Hadoop platforms. Lead prototyping, implement Big Data and ETL processes, coordinate cross‑functional teams, evaluate technical alternatives, and produce progress reports. Serve as primary technical contact and assign/direct team activities to meet business requirements.

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.

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.

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.

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.

Design and build scalable ETL pipelines and real-time dashboards from telemetry and user data. Integrate AI/ML insights, combine internal and external data sources, ensure data quality, and support cross-platform (Android, iOS, macOS, Windows) AI integration to improve consumer protection products and operational monitoring.

Design, build, and support data integration pipelines (batch, API, streaming). Implement ingestion for the data warehouse, monitor and optimize performance, ensure data quality and compliance, collaborate with DevOps/Prod/QA, and provide 24/7 rotating on-call support.

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.