Top Data & Analytics Jobs

Design, build, and scale resilient data pipelines and infrastructure on AWS. Own the data ecosystem using Databricks or Snowflake, write production-quality Python and SQL code, collaborate with cross-functional teams, and prepare data for analysis and product use in a fast-moving startup environment.

Support development and maintenance of data pipelines, ETL processes, and data integrations. Assist with data infrastructure (databases, warehouses, lakes), implement data validation and quality checks, participate in code reviews, document processes, and collaborate with engineers and analysts while learning and following established standards.

Build, operate, and monitor ETL/ELT pipelines between marketing, sales, and operational systems into Databricks. Implement dimensional models, identity resolution, data quality checks, API integrations, and scheduled/event-driven syncs to support segmentation, attribution, and campaign activation. Maintain documentation, alerting, and runbooks.

The Data Engineer will design and build solutions for investment insights using big data technologies, developing data pipelines, and collaborating with cross-functional teams.

Design, architect, and implement scalable, secure AWS-based data pipelines and ETL/ELT workflows. Integrate, transform, and harmonize structured and unstructured data, optimize warehouses/lakes and SQL Server performance, and support dashboards and analytics. Mentor junior engineers, work with customers to clarify requirements, and uphold data governance, quality, and pipeline reliability.

Design, architect, and implement scalable, secure data pipelines and cloud-based data systems (AWS preferred). Lead data architecture for analytics and dashboards, optimize storage and SQL Server performance, integrate and transform diverse data sources, mentor junior engineers, uphold data governance and quality, and support customer-facing analytics deliverables.

As a Data Engineer, you will develop and deploy data solutions, maintain data stores, and create ETL/ELT workflows to organize data for business analysis in a collaborative environment.

The role involves developing scalable data pipelines, automating workflows, optimizing data models, and ensuring data governance while collaborating with various teams.

Design, develop, and maintain data pipelines and ETL workflows in Databricks, collaborating with teams to support large data needs in financial services.

As a Data Engineer, you will design and build robust data pipelines, manage data infrastructure, and support data-driven strategies using advanced engineering techniques.

Looking for a Data Engineer focused on data visualization with experience in handling real-world data and effective communication through data. Extra credit for skills in probabilistic classifiers and full-text search engines.

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

As a Data Engineer I, you will design, build, and operationalize data solutions, manage data governance, and collaborate with teams to optimize analytics processes. Responsibilities include developing pipelines, troubleshooting issues, and mentoring team members.

The Data Engineer will design, build, and maintain scalable data pipelines, develop ETL workflows using Python and SQL, and optimize data processes to ensure reliability and performance.

The Data Engineer is responsible for designing and maintaining data architecture, implementing data pipelines, and collaborating with cross-functional teams to align data strategies with business needs.

Build and maintain AWS-based ETL pipelines (Glue/PySpark, MWAA/Airflow, Lambda) to ingest and process terabytes of financial data into an S3 data lake using Iceberg/Parquet. Implement metadata, monitoring, tests (90% coverage), materialized views (Trino/Athena), CloudFormation deployments, documentation, and support agile delivery and operations for federal clients.

Design, build, and support scalable data platforms and pipelines (ETL, warehousing, APIs) using DBT, Snowflake, and SQL. Collaborate with architecture, security, and application teams, build CI/CD, provide escalated on-call support, mentor staff, and deliver data solutions for the bank.

Design, build, and maintain enterprise-scale data platforms and scalable ETL/ELT pipelines. Develop and operationalize data transformations in Palantir Foundry, debug logs, troubleshoot issues, and convert varied data into usable formats. Guide cross-functional teams, support platform scaling, monitoring, and operations, and apply PySpark/Spark SQL for analytics and reporting to support mission-driven use cases.

Design, implement, and administer operational data flows: ingest, clean, enrich, transform, secure, and move mission-critical data. Perform exploratory data analysis, troubleshooting, and collaborate with customers to ensure compliant, reliable data pipelines.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

Build, maintain, and optimize ETL/ELT pipelines and cloud-based data environments (AWS/Azure). Manage relational databases and data warehouses, ensure data quality, automate workflows, and support aviation analytics and aircraft readiness initiatives at MCAS Cherry Point.

Design and build end-to-end geospatial data systems: ingest and process satellite imagery into columnar/chunked stores, expose results via GraphQL/REST and FastAPI, build Next.js/React dashboards, automate with IaC and CI/CD, orchestrate pipelines with Prefect, optimize petabyte-scale I/O, and lead hiring, mentoring, and engineering best practices.

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.