Top Data Engineer Jobs

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Build and maintain dbt and semantic data models in Snowflake, develop ingestion and transformation pipelines, define trusted product metrics, and improve data collection. Partner with Product and Engineering to support experimentation and feature analysis. Establish data quality testing, documentation, HIPAA-aware access controls, and compliance practices. Use and validate AI-generated code and Snowflake Cortex outputs while raising technical standards across the team.

Investigate and resolve complex data quality, integration, and reconciliation issues across ERP, e-commerce, databases, ETL pipelines, warehouses, and reporting systems. Trace discrepancies to their root causes, remediate and backfill data, improve master-data mappings, automate reconciliation and monitoring, document lineage and dependencies, and establish stronger controls. The role requires independent production troubleshooting, deep SQL Server and T-SQL expertise, cross-functional collaboration, and the ability to improve fragile legacy data processes.

Own the analytics data layer by translating stakeholder needs into certified dimensional models, governed metrics, semantic layers, dashboards, and reports. Build with dbt, SQL, Databricks, Power BI, and Airflow while ensuring data quality, observability, reconciliation, documentation, and dependable orchestration. Partner closely with finance, operations, and client-facing teams, automate recurring workflows, and use AI-assisted engineering practices in a fully remote environment.

Designs, builds, and operates large-scale Hadoop data pipelines and analytics platforms. Responsibilities include ingesting, transforming, and analyzing structured and unstructured data; developing Spark-based processing workflows; working with Hadoop ecosystem technologies, streaming platforms, SQL, and distributed systems; orchestrating workflows; troubleshooting production environments; and supporting reliable, scalable data platforms for analytics, machine learning, and reporting.

Designs, builds, and operates large-scale Hadoop-based data pipelines and analytics platforms. Responsibilities include ingesting, transforming, and analyzing structured and unstructured data; developing Spark and streaming workloads; managing distributed systems, databases, and workflow orchestration; troubleshooting production platforms; and delivering reliable, performant, cost-effective data infrastructure for analytics, machine learning, and reporting.

Design, build, and operate production data pipelines using Python, Spark SQL, and Airflow. Canonicalize healthcare and customer data into trusted internal models, optimize distributed processing, enforce schemas, and implement quality checks and observability. Support customer onboarding, streaming pipelines, troubleshooting, on-call operations, and platform best practices while collaborating with product, analytics, engineering, data science, and customer teams.

Own end-to-end client data migrations: reverse-engineer legacy ETL, design and build Airflow DAGs and Spark jobs, validate and reconcile data at scale, document architecture and decisions, and coordinate cross-functional teams through go-live and hypercare.

Build and maintain scalable data pipelines, models, and cloud infrastructure supporting Xometry’s global marketplace. Collaborate with data scientists, ML engineers, product teams, and business stakeholders to translate requirements into reliable data solutions. Develop self-service tooling, optimize Snowflake and AWS environments, support CI/CD deployment processes, and ensure data integrity, consistency, and usability across internal and external consumers.

Build and maintain scalable data pipelines, models, and cloud infrastructure supporting business performance analysis. Collaborate with data scientists, ML engineers, product teams, and business stakeholders to translate requirements into technical solutions. Develop self-service data tooling, optimize Snowflake and AWS environments for scale and cost, and support deployment through CI/CD and DevOps practices. The role also involves data integration, modeling, documentation, and ensuring data integrity across global entities.

Builds and maintains scalable data pipelines and reusable datasets in Databricks. Ingests, validates, transforms, and models structured and unstructured data using Python, PySpark, and SQL. Integrates warehouse and cloud analytics systems, supports Tableau and Spotfire reporting, improves reliability and performance, and implements testing, monitoring, documentation, and CI/CD. Collaborates with technical and business stakeholders while providing technical guidance, code reviews, and mentorship.

Designs scalable data architectures and pipelines supporting digital twins, analytics, and operational modeling. Develops a digital twin of operating-room environments, translates workflows into data and simulation models, and builds scenario-analysis capabilities for staffing, scheduling, capacity, patient flow, and resource utilization. Creates dashboards, APIs, and decision-support tools while establishing data governance, quality, lineage, validation, documentation, and monitoring. Collaborates with healthcare stakeholders, technical teams, and government leadership.

Designs, builds, deploys, and supports scalable batch and streaming data pipelines across enterprise and cloud sources. Develops governed data models, reusable data products, APIs, automated testing, CI/CD, observability, and production monitoring to support analytics, AI, and operational applications. Partners with technical and business stakeholders to translate requirements into secure, maintainable solutions and communicates technical decisions, risks, and outcomes in Agile environments.

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.

Lead enterprise data strategy and delivery across data platforms, master data management, semantic layers, governance, quality, modeling, and pipelines. The role requires hands-on architecture leadership, executive stakeholder engagement, and delivery of complex multi-region platforms using Snowflake, cloud infrastructure, MDM, Collibra, dbt, and batch and streaming architectures.

Leads enterprise data architecture and platform strategy while designing scalable ETL, batch, streaming, and data warehouse pipelines. Develops solutions with Airflow, Python, DBT, Snowflake, Kafka, Spark, AWS, and SQL. Builds CI/CD pipelines, data quality frameworks, monitoring dashboards, and reliability processes using Jenkins, Datadog, Docker, and Kubernetes. Advises leadership, supports cross-functional data initiatives, and establishes governance and engineering standards.

Build and operate analytics pipelines, data models, data marts, and quality systems for mobile free-to-play game telemetry. Maintain and optimize BigQuery transformation layers, migrate Airflow pipelines to a partner-managed ETL platform, define tracking plans and data contracts, and develop observability capabilities. Partner with studios and analysts to support retention, monetization, and engagement reporting while applying production engineering, documentation, and collaboration practices.

Design, build, and operate big-data and streaming platforms (Kafka, Hadoop, Dremio). Develop, deploy, and monitor data pipelines using Java, Python, Spark/Flink; collaborate on data modeling and ingestion, ensure data integrity and availability, act as a Big Data SME, and perform root-cause analysis.

Administer, architect, optimize, secure, and automate PostgreSQL, MySQL, and Snowflake databases in Azure. The role develops resilient database solutions for healthcare applications, manages performance, access controls, encryption, backups, and disaster recovery, and creates technical documentation. It collaborates across Scrum teams, presents solutions to stakeholders, evaluates emerging technologies, uses approved AI tools, solves complex database challenges, and mentors junior engineers.

Develop, maintain, and support scalable data systems and pipelines for analytics and reporting. Collect, clean, structure, enrich, and govern data from multiple sources while ensuring quality, security, reliability, compliance, and performance. Collaborate with analysts, business analysts, and data scientists to design solutions, deploy optimized pipelines, troubleshoot data issues, and provide technical support.

Develop and optimize enterprise Oracle SQL and PL/SQL solutions, including data models, database designs, APIs, ETL processes, batch jobs, and performance tuning. Collaborate with cross-functional teams to translate requirements into scalable implementations, support testing and CI/CD, troubleshoot production issues, and document solutions. Provide technical leadership, mentor engineers, participate in architecture and code reviews, and drive engineering best practices across data modernization initiatives.

Manage Microsoft Fabric dataflows, pipelines, APIs, external integrations, error reporting, and Azure capacity. Build and maintain scalable data warehouse infrastructure, optimize data access, and support AI and machine learning workloads. Document processes and operational best practices while collaborating with data engineers, BI analysts, and AI specialists.

Participate in AHEAD’s associate development program, completing technical training, certifications, and project shadowing before supporting client data solutions projects. Responsibilities include collaborating across teams, solving complex problems, managing priorities, meeting deadlines, and developing consulting expertise. Candidates should have programming experience, SQL or NoSQL knowledge, willingness to travel extensively, and existing U.S. work authorization. Data platform and cloud experience are preferred.

Design, build, and maintain scalable batch and streaming data pipelines. Write clean code (Python, SQL, Spark), ensure data quality and reliability, monitor and troubleshoot pipelines, document sources and transformations, and collaborate with analysts, data scientists, and product teams to deliver data products and support analytics and ML use cases.

Supports data pipeline development and maintenance for a VA disability claims program. Responsibilities include preparing and cleansing healthcare data, troubleshooting ETL/ELT pipelines, performing data quality checks, documenting workflows, participating in code reviews, and learning claims data systems under senior engineer mentorship.