Top Data Engineer Jobs

Develop, deploy, and support large-scale distributed Big Data applications. Design multi-tier software, participate across the SDLC, perform database design and performance tuning, troubleshoot production issues, and collaborate with internal clients. Work primarily with Hadoop, Spark, Impala, Hive in Unix/Linux and Windows environments within financial/consumer banking contexts.

Develop, deploy, and support large-scale distributed Big Data applications. Design multi-tier software across the full SDLC, perform problem investigation and performance tuning, work in Unix/Windows environments, and collaborate with finance domain teams. Use Hadoop, Spark, Impala, Hive, batch and online processing, shell/Perl scripting, and ETL/job-scheduling tools as needed.

Lead cross-functional analytics initiatives by gathering requirements, prioritizing projects, and guiding Enterprise Data Office pods. Define data requirements, validate analytical outputs, and manage project timelines using Azure DevOps and Power BI. Implement and maintain scalable Azure data pipelines, Snowflake data warehouse enhancements, dbt transformations, SSIS, and integrations. Communicate status to stakeholders and perform post-delivery evaluations to capture lessons learned.

Lead design and implementation of scalable real-time and batch data pipelines for programmatic advertising. Ingest, normalize, transform, and model bid stream, impression, and revenue data; maintain data warehouses and lakes; implement data quality and privacy compliance; support BI teams and optimize yield through analytics and feature engineering.

The Data Engineer will develop end-to-end data pipelines, optimize data architectures, and implement machine learning solutions to enhance business intelligence capabilities.

Provide technical leadership for the MFS data platform, collaborate with cross-functional engineering teams to improve platform adoption, mentor engineers, and design and develop large-scale distributed systems using Big Data technologies and Kafka.

Design, build, and optimize enterprise-scale data pipelines and data models in Databricks to support analytics, AI, and operational systems. Lead MDM initiatives, implement data governance, lineage, and quality frameworks, and collaborate with engineering and business teams to ensure scalable, reliable, and cost-efficient data platforms.

Design, build, and deploy an ETL-based transactional data system to read and manage information state and transmit data via web services. Implement solution from provided design, perform unit testing, deploy to development/test, and collaborate with QA and DPS to resolve defects.

Lead design, development and delivery of enterprise data and analytics solutions using Azure Data Factory, Databricks and Power BI. Manage data warehouse, ETL, stored procedures and data quality; provide technical leadership, stakeholder engagement and agile backlog management to support research and student services.

Design, build, and implement mission dataflows and data pipelines. Develop end-to-end software solutions (front-end, back-end, APIs, infrastructure) from prototype to production, collaborate cross-functionally, identify system improvements, and support future enhancements in an agile environment.

The Data Operations Engineer will monitor and optimize trading processes, enhance integration and deployment pipelines, ensure quality control, and lead continuous improvement initiatives while documenting and reporting on production performance.

The Senior Data Engineer will design and build data pipelines and infrastructure to support data science and business intelligence solutions, utilizing AWS services and streaming technologies.

The Senior Data and Solutions Engineer develops data pipelines, manages analytics frameworks, and collaborates with stakeholders to improve data quality and reporting consistency.

This role involves leading data infrastructure modernization efforts, architecture reviews, and migration planning while providing technical mentorship and establishing operational standards.

The Senior Data Privacy Engineer will design, develop, and support data discovery, privacy compliance, and data protection solutions, focusing on OneTrust and Informatica tools while collaborating across teams and mentoring junior engineers.

As a Senior Data Engineer, you will design and maintain data infrastructure, implement ETL processes, and optimize data workflows using Python and PySpark.

Lead the technical direction for data platforms, develop high-throughput data processing systems, mentor engineers, and ensure production reliability.

Lead design and delivery of enterprise data platforms using Snowflake and AWS. Architect ELT/CDC pipelines, implement Data Vault 2.0 and dimensional models, optimize Snowflake performance, mentor engineers, partner with stakeholders, and ensure operational reliability and observability for analytics and BI workloads.

As a Senior Data Engineer, you'll define data ontology, collaborate with teams, design data pipelines, and optimize data infrastructures to support analytics for Yahoo Mail.

Design, ingest, validate, and manage complex financial and annuity data from external partners. Build and maintain ETL pipelines, SQL queries, and data quality controls; investigate data anomalies; collaborate with Accounting, Actuarial, and Technology; support reporting and process automation to improve financial and regulatory workflows.

Design, ingest, validate, and model complex financial and annuity data from external partners. Perform SQL-based ETL, data quality checks, root cause analysis, and collaborate with Accounting, Actuarial, and Technology to support reporting, valuations, and process automation.

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, build, and operate a privacy-safe, cloud-based data platform supporting batch and real-time pipelines, analytics, ML/AI, governance, observability, and high pipeline reliability. Partner with stakeholders, set data engineering standards, troubleshoot production incidents, and mentor engineers.

Lead design and implement scalable data infrastructure and transformations. Build data repositories, convert data science algorithms into robust pipelines, mentor a small data engineering team, ensure delivery quality, and collaborate with Product, Data Analysts, and Data Scientists.

Design and implement scalable data infrastructure and production data transformations. Lead and mentor a small Data Engineering team, ensure quality delivery, collaborate with Product, Data Analysts, and Data Scientists to operationalize algorithms and build robust data repositories.