Top Data & Analytics Jobs

Lead design and implementation of scalable Databricks ETL/ELT pipelines and data architectures for marketing analytics, manage 1-2 projects, liaise with stakeholders, ensure data quality, monitoring, and delivery of analytics-ready datasets.

Design, build, and maintain scalable ETL/ELT pipelines, data warehouses, and data lakes. Ensure data quality, governance, and security while enabling BI and analytics. Provide technical leadership, mentor junior engineers, and collaborate cross-functionally to drive architecture and implementation.

Design, build, and maintain scalable real-time and batch data pipelines and platform architecture. Ensure data quality, enable BI/AI/ML productionization, mentor engineers, and deploy data products and ML models.

Lead a team of data engineers to design, build, and maintain scalable data pipelines, a Data Vault, data warehouses and marts. Own backlog, sprint planning, mentoring, stakeholder collaboration, data validation/monitoring, and platform improvements to deliver key metrics and data infrastructure across the business.

Design and build a single data platform and pipelines, ensure infrastructure and standards, provide governance and consultancy, and work with stakeholders to expand data capabilities across the insurance business.

Design and build advanced data visualizations for complex datasets within a Sales Analytics platform. Develop interactive, data-driven web applications (SPA) using D3.js, AngularJS, WebGL and modern front-end technologies to enable business decision-making.

The role involves building data platforms and pipelines, integrating AI solutions, and collaborating with product owners to address complex client issues.

Own and modernize the legacy SQL data estate: support and stabilize SQL Server databases, build and modernize ELT pipelines (including SAP integration), create analytics-ready data models, ensure HA/DR and recovery objectives, and operationalize data governance and privacy (GDPR/CCPA). Serve BI, reporting, and AI consumers while partnering with architecture, DBA, and GRC teams.

Build and maintain enterprise data platform and ETL/ELT pipelines, design and deploy data models, ensure data quality and governance, integrate third-party sources, and support analytics and AI initiatives across the organization.

Design, develop, and maintain enterprise ETL pipelines and scalable data processing using ADF, Informatica, Python, Spark/Databricks, and relational systems. Support Medicaid analytics, data validation, cloud migrations, production operations, and CI/CD practices.

Design, build, and maintain a petabyte-scale open-source data lakehouse and end-to-end data pipelines (ingest → transform → consume) ensuring performance, reliability, and data quality for analytics workloads.

Design and own end-to-end data platform architecture, build ingestion and transformation pipelines, model multi-tenant datasets, optimize Postgres and ClickHouse performance/cost, implement orchestration and observability, define data quality and reconciliation standards, and mentor data/product engineers.

Own and modernize the legacy SQL data estate: support and stabilize SQL Server databases, design HA/DR and recovery plans, build resilient ELT pipelines from sources including SAP, create analytics-ready data models for BI/AI, operationalize data governance and privacy (GDPR/CCPA), and mentor DBA succession while delivering documentation and runbooks.

Build and operate production data infrastructure: ingest, transform, and route large-scale streaming and batch data from heterogeneous enterprise sources; design ontology/semantic models and tuple/graph representations; ensure isolation, data quality, observability, and low-latency delivery for ML and product teams.

Design, build, and maintain data architectures, ETL pipelines, and data models to enable analytics across Marketing, Finance, Sales, and Logistics. Implement integrations from cloud sources, secure API connections, perform large-scale data mining and entity resolution, translate algorithms into code, and use SSIS and BI tools to deliver reliable reporting and dashboards.

Design, build and scale a 500+ TB Audience Management Big Data platform. Own data ingestion, validation, transformation and storage pipelines. Write server-side Spark/Scala code, perform R&D on new technologies, mentor engineers, and partner with product managers to meet roadmap and business needs.

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.

Design, build, and maintain scalable, fault-tolerant identity and device data pipelines and backend systems. Modernize data models, improve code quality and test coverage, support production operations and on-call, collaborate with product and cross-functional teams, and participate in code reviews and agile ceremonies.

The Sr. Data Engineer will develop data pipelines, automate data processing, collaborate in Agile teams, and ensure data extraction reliability while adhering to engineering principles.

The Sr. Data Engineer will manage data pipelines, develop ETL processes, support continuous automation, ensure data reliability, and work collaboratively in Agile teams to meet client needs.

The Senior Data Engineer will oversee data operations, design scalable data pipelines, ensure data quality, and support cross-functional data initiatives.

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.

The Data Engineer will design and maintain data pipelines, enhance the data warehouse, support machine learning applications, and collaborate with various teams to leverage data effectively.

As a Data Engineer at MAI, you will develop and maintain data platforms, design ETL/ELT processes, ensure data quality, and collaborate with teams to optimize data utilization for analytics and ML applications.