Top Data Engineer Jobs

Leads data and analytics engineering for Zero Trust maturity assessment and implementation measurement. Designs common data models, integrates and validates enterprise data, develops maturity and risk metrics, supports prioritization and gap analysis, and establishes data quality, lineage, traceability, and documentation practices. Partners with dashboard and automation specialists to streamline reporting and data refresh workflows.

Design, build, and optimize scalable data platforms, pipelines, and lakehouse/warehouse solutions using Microsoft Fabric and Azure. Implement ETL/ELT, data migration from SQL Server, governance, CI/CD, streaming integrations, and Power BI connectivity to enable analytics and AI initiatives while ensuring security and performance.

Designs, builds, and optimizes data pipelines and architectures (ETL/ELT) to support analytics and ML teams. Assembles complex datasets, implements process improvements, collaborates with IT on AWS and Snowflake infrastructure, applies statistical techniques, supports reporting and analytics, and ensures compliance with financial regulations.

The Senior Data Engineer will design and develop scalable data architectures, manage ETL processes, optimize databases, and implement MLOps pipelines, promoting data-driven decision-making across the organization.

Lead technical design and implementation of production-grade data platforms, ETL/ELT and streaming pipelines, containerized Python data applications, Snowflake and dbt modeling, observability, CI/CD, and AI/ML data integrations to enable scalable data products and business-aligned analytics.

Lead design and build of Snowflake-native medallion data architecture, ETL/ELT pipelines, master data and governance, and an enterprise semantic layer. Partner with Finance to produce reliable financial data products, optimize performance/cost, enforce data quality and compliance, mentor junior engineers, and support AI use cases and observability.

Own and scale streaming Kafka pipelines and large-scale Spark batch jobs processing billions of rows. Drive pipeline reliability, observability, data modeling, and cost/latency optimizations. Partner with data scientists and platform engineers, define SLOs, improve efficiency, contribute to shared infrastructure (Airflow, libraries, data catalog), and mentor junior engineers.

Lead design, maintenance, and optimization of on-prem and cloud data platforms. Perform DBA duties for SQL Server, manage HA/DR and replication, troubleshoot performance, lead on-prem to Azure migrations, maintain Azure data services and Microsoft Fabric, optimize cloud cost/performance, implement monitoring, collaborate with architects and BI teams, and mentor junior engineers.

Provide day-to-day administration and production support for Snowflake, Matillion, and related data platforms. Monitor jobs and performance, troubleshoot ETL/ELT and report refresh failures, manage access and deployments, maintain runbooks, and build AI-driven automation and agent-based workflows to improve operational reliability and efficiency.

Design, develop, and maintain batch and streaming data pipelines and ETL for large unstructured datasets. Build and operate CI/CD pipelines, containerize applications with Docker, deploy on Kubernetes, and collaborate with developers, data scientists, SMEs, and cyber analysts to deliver data management solutions.

Own and operate the Snowflake warehouse and Sigma semantic layer; manage ingestion (Fivetran), dbt model scoping and review, reverse-ETL, data quality SLAs, dashboards, month-end financial analytics, vendor management, and automation to support AdOps, Sales, Finance, and executive reporting.

Design, implement, and maintain automated ingestion and transformation workflows to bring spreadsheet-based operational data (JSON/CSV/Excel) into enterprise databases and Pega workflows. Validate data integrity, troubleshoot pipelines, support reporting integrations, document data flows, and collaborate with architects, developers, and stakeholders to modernize and secure enterprise data services.

Own and scale Provable's data platform on GCP: build and optimize ingestion pipelines from blockchain indexers, design privacy-aware data models, manage Postgres and BigQuery, develop high-performance data-serving APIs, ensure data quality and observability, and use IaC (Terraform, Helm) and Kubernetes to deploy and cost-optimize environments.

Design, build, and maintain scalable ETL/ELT data pipelines and data platform architecture. Build programmatic pipelines (primarily Python), optimize analytical data models (SQL), integrate sources into Snowflake, use orchestration/transformation tools (dbt, Airflow, Dagster), improve data quality/observability, and partner with product, engineering, and analytics to deliver reliable data for decision-making.

Design, develop, test, and maintain ETL solutions and data warehouse structures using SSIS and SQL Server. Build and support SSAS models, optimize ETL performance, ensure data quality, partner with stakeholders, and produce technical documentation to support BI and reporting initiatives.

Design, build, and operate scalable ingestion, curation, and metadata-enrichment pipelines for enterprise data catalog and discovery. Ensure data quality, schema evolution, lineage, and cost-efficient large-scale processing; translate technical concepts for non-technical stakeholders and support downstream mission use.

Design and maintain cloud-native data platforms (lakes, lakehouses, warehouses) on AWS/Azure/GCP. Build CI/CD templates and IaC for self-service, develop ETL/ELT for batch and streaming, enforce RBAC, encryption, data lineage and compliance, monitor performance and cost, and collaborate with architects, developers, and data scientists to integrate APIs and deliver end-to-end data products.

Lead architecture and execution of Faire's data platform and analytics capabilities. Build and operate scalable, secure ETL/ELT, streaming, storage, compute, orchestration, and data warehousing at petabyte scale. Drive performance, cost, monitoring, governance, and self-serve analytics; influence cross-functional teams and mentor engineers.

Design and maintain a dbt-based business data layer for GTM metrics, centralize and reconcile multi-source data, implement automated data quality and reconciliation checks, align and document metrics with stakeholders, mentor analytics team, and lead root-cause investigations into complex data anomalies.

Design and build production-grade data pipelines on Databricks and AWS extracting from enterprise ERPs, implement medallion architectures, ensure data quality/governance, collaborate with business SMEs, and deliver AI-ready data products.

Leads enterprise data ingestion and integration for AI, machine learning, Generative AI, analytics, and BI workloads. Designs scalable pipelines for structured, semi-structured, unstructured, and multimodal data; establishes quality, governance, lineage, security, and observability standards; and supports RAG, AI Search, vector retrieval, and MCP capabilities. Partners with technical and business stakeholders, mentors engineers, evaluates emerging technologies, and drives production delivery and continuous improvement in a regulated financial-services environment.

Design, build, and optimize scalable ETL/ELT data pipelines using Databricks and cloud platforms. Implement data quality checks, monitor and troubleshoot jobs, audit pipelines for compliance, and collaborate with analysts and business stakeholders to define data models and support reporting.

Build and maintain end-to-end event-driven analytics pipelines for telemetry from robots and cloud systems. Own metrics catalog, schema evolution, data quality validation, and performant analytical models and queries to enable reporting and self-service analytics.

Build and own production data pipelines and financial data models to turn payments and operational data into trusted metrics and actionable insights. Ship features for Whop Financial Reports, standardize financial semantics, ensure data quality and lineage, and enable self-serve analytics across Finance, Product, GTM, and Exec teams.

Build, maintain, and monitor batch, streaming, and replication data pipelines; translate business needs into data solutions; enforce data modeling, orchestration, and quality best practices; participate in code reviews and knowledge sharing; enable self-service curated data products and semantic layers for analytics and compliance.