Top Data Engineer Jobs

Design, build, and optimize large-scale Databricks/Spark/Delta Lake data pipelines and Redshift data platforms. Implement ETL/ELT patterns, ingest telemetry and behavioral data (Matomo, FullStory, Pendo), improve performance and observability, provide L2/L3 production support and RCA, and mentor engineers while shaping data architecture and best practices.

Design, build, deploy, and maintain large-scale data pipelines and ETL using Python and Spark on Azure (Synapse/Databricks). Collaborate with cross-functional teams to translate requirements into data designs, implement CI/CD, monitor and troubleshoot production pipelines, ensure data security and governance, perform code reviews, and document solutions while driving automation and process improvements.

As a Data Engineer, you will build and scale data pipelines, optimize advertising effectiveness, and handle large datasets using AWS, Airflow, and Snowflake.

Design and maintain data models, build and optimize data pipelines and database infrastructure, integrate data with BI tools, ensure data quality and governance, troubleshoot performance and downtime, and collaborate with stakeholders to deliver reliable analytics and reporting.

Design, build, and maintain cloud-based data pipelines and ETL processes to populate data warehouses. Optimize storage and performance, enforce security and data quality, automate tasks with scripting, mentor junior engineers, and evaluate new technologies to improve scalability and reliability.

Design, build, and maintain cloud-based data pipelines and ETL into data warehouses (Snowflake/AWS). Ensure data quality, security, and performance; optimize storage and retrieval; automate tasks with Python/Bash; monitor/troubleshoot pipelines; mentor junior engineers; evaluate new technologies and support legacy systems.

Lead design and delivery of enterprise BI for Finance: architect Power BI solutions, build data models and dashboards, develop complex SQL transformations, ensure performance and governance, align stakeholders, and mentor teammates.

As a Lead Data Engineer, you will design scalable data platforms, optimizing pipelines, defining data models, and guiding data integration while collaborating with cross-functional stakeholders to align strategies with business goals.

Designs and implements cloud-based single and multi-tenant data solutions, PoCs, real-time pipelines and ETL/ELT processes using Snowflake, AWS, Python and Informatica. Builds and tunes databases and stored procedures, develops APIs with Spring Boot/Java/Python, applies CI/CD, leads architecture and QA, manages project lifecycle, advises on risk and technical strategy, and mentors junior engineers.

Designs and maintains scalable ETL/ELT pipelines to migrate and process large datasets into Snowflake using dbt, SQLMesh, Python, and SQL. Automates deployments and testing with Jenkins, GitHub, and PyTest. Builds data solutions in Agile teams, optimizes data delivery, creates runbooks/documentation, and works with Alteryx, Tableau, and Actimize. Experience with financial crime typologies, AML/BSA, or fraud detection is required.

Designs and delivers data lakes, data warehouses, and reporting platforms. Builds and maintains ETL/ingestion pipelines into Snowflake, supports multiple source databases, implements data masking and reconciliation, and automates CI/CD deployments using AWS, Python, Jenkins/Concourse and related tools. Performs testing, documentation, post-install validation, and project planning across multiple initiatives in a financial services environment.

Design, implement, tune, and maintain scalable, secure database systems and ETL/data pipelines (Oracle, Snowflake). Build CI/CD deployments, data integration, and BI reports using Informatica, SnapLogic, Airflow, Jenkins, and Power BI/OAC. Mentor staff, define architecture, troubleshoot production issues, and produce documentation across the SDLC in a financial services environment.

As a Lead Data Engineer, you will design and implement data solutions using big data technologies, collaborate with Agile teams, and mentor other engineers.

The Senior Data Engineer manages Oracle databases, performs data migrations, conducts performance tuning, and develops automation. This role also includes on-call support and collaboration with teams.

Design, build, and support data infrastructure and pipelines for operational and analytics workflows. Collaborate with product, engineering, and customers to deliver scalable, secure, performant data products; own BI, streaming, ETL, and deployment practices; promote trusted, accessible data and drive data team outcomes.

Design and build enterprise-scale Snowflake environments, develop and optimize data pipelines with Python, dbt, and Airflow, lead client technical strategy, implement Snowflake security/compliance, and mentor junior engineers while collaborating with Snowflake, AWS, and Anthropic partners.

Design, build, and maintain scalable data pipelines, lakes/warehouses, and platforms. Optimize cloud storage and pipelines, enforce data governance, guide multidisciplinary teams, and support mission-driven analytics with streaming and batch workflows.

Build and maintain data marts, materialized views, and pipelines in an AWS Aurora PostgreSQL environment. Integrate data with downstream BI platforms (MicroStrategy), design/optimize workflows for analytics and reporting, monitor pipeline health and data quality, and support federal data security compliance (FISMA/NIST 800-53). Collaborate with engineering and product teams to enable data-driven features.

As a Data Engineer, you will design, maintain data pipelines, collaborate with stakeholders, and optimize data solutions across various departments.

The Data Engineer will build and maintain ETL/ELT pipelines, set up a data warehouse, ensure data quality, and collaborate with stakeholders to create usable datasets.

As a Senior GCP Data Engineer, you will design data pipelines, implement data governance, mentor team members, and ensure data quality and integrity.

As a Data Engineer, you will create efficient data models, pipelines, and analytics frameworks, collaborating with various departments to transform raw data into actionable insights.

The Principal Data Engineer will lead the design and development of scalable data platforms, optimize data pipelines, and collaborate with multiple teams to enhance data solutions.

Design and maintain scalable ETL/ELT pipelines, develop data solutions using Databricks and Snowflake, and collaborate with cross-functional teams to ensure data quality and performance optimization.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.