Top Data & Analytics Jobs

Serve as a senior technical lead for data engineering consulting: work with customers, design complex data models, write and review efficient code, resolve complex bugs, mentor engineers, lead cross-functional development, and plan/execute automated and performance tests to deliver high-quality data solutions.

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.

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.

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.

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.

The Data Engineer II designs, develops, and maintains data integration and analytics solutions, ensuring data quality and accessibility for business stakeholders, while collaborating with teams to meet business needs.

The Data Engineer will design, develop, and implement data solutions, ensuring data integrity and availability, collaborating with stakeholders to meet business analytics needs.

The Data Engineer will design and implement ETL frameworks, perform data analysis, troubleshoot data issues, and optimize data models to meet business needs.

Lead large-scale migration of document-heavy legacy systems into cloud, convert unstructured documents to analytics-ready data, build MuleSoft integrations and APIs, apply DataWeave transformations, ensure data quality, lineage, governance, and support Salesforce Data 360 implementation and cutover in Agile teams.