Top Data Engineer Jobs

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.

Leads the design, automation, optimization, and maintenance of scalable data pipelines using Airflow, dbt, Python, SQL, and Google Cloud technologies. Manages dbt models, develops reusable architecture patterns, monitors pipeline performance, supports CI/CD and testing, and ensures data security and governance. The role also provides technical leadership, mentors engineers, collaborates cross-functionally, documents implementations, and drives continuous improvement across data engineering practices.

Senior Data Engineer responsible for designing, building, and optimizing enterprise data pipelines, architectures, and scalable reporting solutions. The role integrates data from diverse business platforms, develops Snowflake and dbt solutions, implements governance and security, and enables self-service analytics. It partners with executives, product teams, analysts, developers, and data scientists while mentoring team members, evaluating emerging technologies, and delivering reliable data assets and actionable insights.

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, 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.

The Senior Data Engineer will design, build, and optimize data pipelines and models, lead a team, and work with stakeholders to deliver data-driven solutions for a Federal client.

Lead the design and delivery of data contracts and Delta Sharing across JLL’s enterprise data platform. Build governed, self-service data access, automated quality validation, shared data models, APIs, testing, and reliability capabilities. Integrate these solutions with ingestion, storage, and API frameworks; mentor engineers; collaborate with technical and business stakeholders; and create documentation that enables broad adoption of standardized data-sharing practices.

Designs, develops, tests, documents, and deploys scalable data architectures, big data platforms, ETL pipelines, and data warehouse solutions. Improves process automation, infrastructure scalability, data reliability, and quality. Troubleshoots complex data issues, performs validation and performance tuning, and collaborates with developers, architects, analysts, data scientists, and functional teams to deliver business-aligned data solutions.

Build and lead scalable data platforms, ETL frameworks, streaming pipelines, data lakes, warehouses, and transformation tools. Develop Python- and Spark-based systems for extracting, validating, transforming, and analyzing semiconductor testing data. Support self-service machine learning and analytics platforms, real-time processing, custom visualizations, and complex data ecosystems. Collaborate with IT and business teams to define data strategies, gather requirements, plan projects, and deliver production-ready solutions.

Designs and leads scalable data platforms, event-driven services, orchestration pipelines, and DataOps practices using Python, Airflow, Snowflake, Kafka, and related technologies. The role establishes engineering standards, governance, security, observability, cost efficiency, and AI-assisted development practices. It also mentors engineers, drives cross-functional initiatives, modernizes data ingestion, and ensures reliable, compliant, and well-governed systems.

Build and own production data pipelines using Snowflake, Airflow, dbt, and Python; implement canonical data models; embed validation, quality checks, lineage, freshness, and alerting; support governed self-service data delivery; migrate workloads; partner with business consumers; participate in on-call and incident reviews; and mentor engineers while maintaining strong testing, CI/CD, documentation, and code-review practices.

Leads the design, development, and implementation of scalable data pipelines, APIs, cloud-based integrations, and Customer Data Platform solutions. Builds unified customer data ecosystems supporting analytics, personalization, and enterprise integration. Responsibilities include data modeling, Lakehouse and API optimization, governance, privacy compliance, CI/CD automation, monitoring, troubleshooting, documentation, and cross-functional collaboration. Requires extensive experience with Python, SQL, REST APIs, cloud platforms, data integration tools, CDPs, and modern data engineering architectures.

Designs and manages enterprise AI data foundations, including data models, governance, quality standards, pipelines, integrations, access patterns, versioning, and lineage. Partners with CIO, data architecture, and engineering teams to make structured and unstructured data secure, trusted, discoverable, and accessible for AI and analytical workloads. Requires hands-on experience with SQL, databases, ETL/ELT, cloud data platforms, business intelligence, data warehousing, and lakehouse architectures in government, defense, or large enterprise environments.

Owns the end-to-end design and development of a greenfield data and AI platform for federal background investigations. Responsibilities include lakehouse architecture, ingestion and transformation pipelines, Terraform infrastructure, CI/CD, governance, cataloging, lineage, access control, compliance with FedRAMP and NIST requirements, artifact promotion, platform reliability, cost management, and enabling data science and machine learning teams.

Lead database engineering for a federal species-data integration program in a hybrid AWS cloud environment. Responsibilities include harmonizing schemas, documenting source-to-target mappings and lineage, enforcing metadata and data-quality standards, implementing encryption and NIST-aligned security controls, building authoritative source registries, optimizing performance, and leading production migration, validation, monitoring, and operational documentation. The role requires Databricks or equivalent lakehouse expertise, ETL programming, federal governance knowledge, and database migration leadership.