Top Data & Analytics Jobs

Lead Data Engineer will design, build, test, and support cloud-based data solutions and streaming systems. Collaborate with Agile teams and product managers, mentor engineers, implement big data tooling (Spark, Kafka, EMR), and optimize data warehousing (Redshift, Snowflake) using Java/Scala/Python and SQL. Ensure code quality with unit tests and reviews.

Lead Data Engineer designs, develops, tests, implements, and supports cloud-based data solutions. Collaborates with agile teams, mentors engineers, builds streaming and batch pipelines using big-data and cloud technologies, and ensures performant, well-tested code.

Lead Data Engineer designs, builds, tests, and supports cloud-based data platforms for marketing and messaging. Collaborates across Agile teams to deliver scalable, real-time data and ML-enabled solutions using distributed systems, data warehousing, ETL, and streaming technologies. Mentors engineers and participates in code reviews and performance tuning.

As a Sr. Lead Data Engineer, you'll collaborate across teams to develop technical solutions, lead developers, and utilize big data technologies and cloud services to support data transformations.

Lead Data Engineer designs, develops, tests, and supports cloud-based data solutions. Collaborates with Agile teams on distributed, real-time, and ML-enabled systems using languages and data platforms (Java, Scala, Python, Go, Redshift, Snowflake). Mentors engineers, performs code reviews and unit tests, and drives technical innovation and experimentation.

Support building and maintaining ETL/ELT data pipelines and cloud data workflows. Work with senior engineers and cross-functional teams to implement, test, deploy, and document data processes. Continuously learn cloud technologies, SQL, and Python/PySpark, follow coding best practices, and contribute to process improvements in an Agile environment.

Design, build, and maintain scalable data pipelines and ETL/ELT frameworks using Python and PySpark. Develop reusable components, microservices, and cloud-based data solutions on AWS and Snowflake. Ensure code quality via reviews, CI/CD, logging, and best practices while collaborating with stakeholders to solve data problems.

Design, build, and maintain production data pipelines, perform ETL and transformations, create data models and warehouses, automate reporting, ensure data quality and security, troubleshoot workflows, collaborate with clients and teams, and produce technical documentation.

Design, build, and support cloud data solutions on Azure: develop ETL pipelines, transformations (SQL/Python/PySpark), data models, storage patterns, quality checks, monitoring, and CI/CD; collaborate with stakeholders and document architecture and runbooks.

Design, build, and maintain scalable ETL/ELT data pipelines and data warehouses using Databricks, Spark, SQL Server, SSIS, and PL/SQL. Integrate healthcare data, optimize queries and performance, monitor data quality, support HIPAA compliance and data governance, and collaborate with stakeholders to deliver analytics-ready data assets.

Design, build, test, and operationalize data pipelines and solutions using Databricks and SQL. Ensure data quality, governance, and stakeholder collaboration to enable BI and AI/ML analytics; troubleshoot, document, and support data products.

Lead design, build, test, and support cloud-based data platforms and streaming systems. Collaborate across agile teams to deliver scalable ETL, real-time processing, data warehousing, and mentor engineers while ensuring code quality and performance.

Design, build, and maintain cloud data infrastructure and ETL/ELT pipelines using Microsoft data technologies. Develop and optimize T-SQL scripts and star-schema data models, support data quality, governance, and security, and participate across the full lifecycle from requirements to deployment and monitoring.

Design and implement scalable data architecture and storage (cloud/on-prem), build and maintain ETL pipelines, enforce data governance and security, ensure data quality, evaluate technologies, collaborate with analysts and data scientists, and document data assets.

Build and maintain scalable pipelines to ingest, clean, and integrate large multimodal datasets. Own ingestion across APIs, files, cloud storage, and warehouses. Design linkage, entity resolution, deduplication, and schema harmonization. Implement data quality checks, validation, and documentation. Collaborate with engineering, research, and deployment teams to prepare integrated data for simulations and evaluate new data sources.

Build and maintain low-latency, real-time analytics and ETL pipelines for sports betting products. Support production systems during live events, integrate complex datasets, develop predictive analytics and enterprise APIs, and contribute to automated sports data delivery frameworks.

Senior consulting data engineer responsible for leading complex data engineering projects, coding robust solutions, designing complex data models, troubleshooting and optimizing data operations, mentoring engineers, interfacing with customers and cross-functional teams, and ensuring quality through testing, code reviews, and performance tuning to deliver on-time, on-budget solutions.

Build and maintain data pipelines and dbt models, own reporting and dashboards across Finance, GTM, Product, and Engineering, support third-party financial data integrations, enable GTM and customer-facing analytics, and ensure data quality, testing, and documentation.

Design and deliver cloud-based data architectures, ETL/ELT pipelines, and data lake/warehouse solutions for a Department of War client. Lead a small engineering team, implement migration strategy, build live-data dashboards, recommend tools, and coordinate with government PM. Hybrid role at Fort Bragg with quarterly travel; requires active Secret clearance and US citizenship.

Lead deployment and administration of the electronic Quality Management System (eQMS), digitize quality workflows, and build analytics and BI solutions (Power BI) to monitor quality KPIs. Partner cross-functionally to implement automation, support UAT/validation/training, maintain data integrity and governance, and enable future predictive quality and AI-enabled decision support.

Build, maintain, and troubleshoot data pipelines moving data into Snowflake. Write dbt models with tests/docs, backfill and validate data, investigate upstream data issues, run incident response for MSSQL/Snowflake/S3, and document processes.

As a Data Engineer, you will design and maintain data pipelines, support DoD systems, and ensure data quality and compliance.

Senior Addepar data engineer/analyst responsible for supporting and scaling Addepar integrations, building and maintaining data pipelines (Addepar↔Snowflake/Canoe/other platforms), diagnosing and resolving data quality issues, developing SQL-based reconciliations and automation, partnering with business and technology stakeholders, and supporting production operations, governance, testing, and documentation for wealth and asset management data.

Design, build, and maintain scalable data pipelines and architectures. Perform data extraction, cleansing, transformation, and ensure data quality and accessibility. Collaborate with data scientists, analysts, and product teams to deliver data solutions and troubleshoot data issues.

Build and maintain cloud-native data pipelines and architectures (warehouse, lakehouse, streaming) to support BI, analytics, ML, and GenAI. Collaborate with data scientists and stakeholders to produce AI-ready datasets, enforce data quality and governance, optimize performance and cost, and implement ingestion, ELT, and observability practices under mentorship.