Top Data & Analytics Jobs

Develops Python data pipelines and business data objects for applications. Designs, implements, tests, and deploys software and CI/CD pipelines, performs source-to-target mapping, resolves complex technical issues, and promotes software quality practices. The role requires collaboration across functional teams, independent execution of complex assignments, and experience with cloud platforms, Snowflake, version control, and AI coding tools.

Build and maintain scalable fraud data pipelines, Snowflake data marts, dbt models, and summarized metric tables. Partner with fraud analysts and engineering teams to enable reporting, scorecards, monitoring, and operational decision-making. Establish data engineering standards, documentation, testing, governance, data quality, and pipeline reliability for audit-ready fraud analytics infrastructure.

Designs reusable data ingestion and transformation components, automates data quality, governance, testing, lineage, and deployment workflows, and improves pipeline observability and reliability. The role supports monitoring, incident response, cloud and container integrations, infrastructure as code, documentation, reusable datasets, and Agile platform improvements. Candidates need five or more years of data engineering experience plus expertise in orchestration, distributed processing, storage, scripting, automation, and version control.

Implements data pipelines and platform features, automates validation and operational tasks, supports CI/CD and infrastructure-as-code updates, monitors platform reliability, assists with cloud configuration, upgrades, migrations, and performance tuning, and maintains technical documentation. The role also supports incident escalations and recommends incremental improvements. Requires data engineering or software/platform experience, data modeling and orchestration knowledge, scripting, version control, and a bachelor’s degree or equivalent experience.

Designs reusable data ingestion and transformation components, automates data quality, governance, lineage, and deployment checks, and improves pipeline reliability through monitoring and observability. The role supports cloud integrations, container orchestration, infrastructure as code, documentation, Agile planning, and incident response while partnering with data consumers to deliver usable datasets. Requires 5+ years of data engineering experience, distributed processing and storage expertise, scripting, automation, and version control skills.

Leads enterprise data engineering strategy, big data platforms, cloud development, data integrations, and data products across T-Mobile and partner brands. Oversees technology roadmaps, platform lifecycle, data quality, security, analytics alignment, and organizational transformation. Builds and manages engineering and analytics teams, advances Agile delivery, develops a technical Center of Excellence, evaluates emerging technologies, and translates business needs into scalable data solutions.

Design, build, and optimize end-to-end data pipelines and architectures using Databricks, Spark, AWS, Airflow, and Kafka. Develop ETL processes, data models, warehouses, and reliable Python and SQL solutions while ensuring data quality, security, and governance. Collaborate with data scientists, analysts, and stakeholders to support analytics and AI/ML initiatives. Monitor workflows, troubleshoot issues, validate AI-assisted outputs, and improve performance and resiliency.

Design, build, and maintain data solutions for Retail Bank. Mine varied data sources, develop BI dashboards and metrics, drive analysis for business insights, and enforce data quality, lineage, and governance working with tech teams and Snowflake/AWS platforms.

Partner with stakeholders to gather requirements and deliver reporting, business applications, workflow automation, and AI-driven solutions. Build Power BI dashboards, reports, and semantic models; develop Power Apps and Dataverse applications; create Power Automate workflows and Copilot Studio agents; and collaborate on testing, troubleshooting, implementation, adoption, and ongoing support.

Build and maintain Terraform-managed Databricks data platforms on AWS, develop secure external data-sharing capabilities, implement data governance controls, automate audit evidence collection, and improve platform reliability, performance, monitoring, and cost efficiency. The role also involves incident resolution, on-call participation, data pipeline development, compliance collaboration, and designing secure-by-default infrastructure for publisher partners.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Design and develop data pipelines, integrations, transformations, and infrastructure across AWS, Snowflake, Airflow, and MySQL. Troubleshoot warehouse issues, validate data quality, improve pipeline reliability, and translate business needs into data engineering solutions. Interpret technical findings for client recommendations, manage workstreams, review junior team members’ work, and provide technical guidance across managed services engagements.