Top Data & Analytics Jobs

Develop and maintain Python-based data analytics applications, pipelines, integrations, dashboards, and data quality solutions for Prime Services financial data. Architect scalable systems using Kafka, Spark, S3, Trino/Starburst, and databases. Translate requirements from business stakeholders and quantitative analysts into production software, support global batch operations, troubleshoot incidents, and improve monitoring through AI-powered agents. Ensure data accuracy, lineage, regulatory compliance, and operational resilience across distributed financial technology teams.

Build and maintain reusable data structures, automated Microsoft Fabric pipelines, SQL logic, and governed reporting foundations. Modernize SSRS reporting by centralizing duplicated logic, troubleshoot data-quality and pipeline issues, and support traditional and AI-assisted business insights. Partner with technical and business stakeholders to translate recurring reporting needs into scalable solutions while maintaining documentation and operational practices.

Own end-to-end customer data migrations from legacy databases and files into GovPilot. Use AI coding agents to profile data, create mappings, generate transformation and loading scripts, automate validation, and build reusable migration pipelines. Review and correct AI-generated SQL and scripts, identify data quality issues, maintain auditable migration logic, manage multiple client migrations, and support evening or weekend production go-lives.

Leads complex data engineering workstreams and mentors engineers while designing scalable pipelines and cloud data infrastructure. Integrates marketing data from GA4, advertising platforms, CRMs, and CDPs into reliable datasets. Collaborates with machine learning teams on model training and serving, establishes engineering and data quality standards, and applies AI-enabled development workflows. Partners with stakeholders to deliver product-minded data solutions and drives process improvements, documentation, monitoring, and knowledge sharing.

Designs, builds, and optimizes scalable Databricks-based data platforms, pipelines, integrations, and reusable engineering tools. The role focuses on platform performance, reliability, cost efficiency, security, governance, automation, and cloud infrastructure. It collaborates with engineering, analytics, security, and other stakeholders on architecture, technical planning, production support, and continuous improvement. The position requires hands-on data engineering experience with Databricks, cloud services, Python, SQL, Spark, infrastructure-as-code, and CI/CD practices.

Build and operate a governed Snowflake enterprise data warehouse, including ingestion, orchestration, dbt transformations, infrastructure as code, security controls, reliability checks, and CI/CD. Partner with data owners and BI teams during migration from legacy systems, ensure compliance for sensitive financial data, support reconciliation and incident resolution, and mentor engineers. The role requires hands-on ownership of pipelines and platform components across development and production environments.

Develop scalable data processing and analytics tools for engineering and business data. Translate stakeholder problems into analytical requirements, build end-to-end ETL and relational data solutions, create user interfaces, perform exploratory analysis, detect trends and anomalies, and communicate actionable insights through effective visualizations and reporting. Collaborate with technical and programmatic teams while following production coding, version-control, and development lifecycle practices.

Plans and executes enterprise data migrations, conversions, transformations, archival, and cloud data platform operations. Builds ETL/ELT pipelines and migration scripts, performs profiling and source-to-target mapping, validates data, manages cutovers and rollback procedures, and troubleshoots migration issues. Optimizes cloud platforms, databases, workloads, security, access, cost, and performance while collaborating with engineering, governance, architecture, and business teams.

Serves as a hands-on technical lead for enterprise data engineering across multiple value streams. Designs and implements data pipelines, ETL/ELT processes, warehouses, marts, models, and integrations across cloud and on-premises systems. Makes technical design decisions, conducts code reviews, solves complex engineering challenges, establishes best practices, mentors engineers, and partners with architects, product owners, analysts, and stakeholders to deliver scalable, secure, reliable data solutions.

Lead the architecture and development of Penn Foster Group’s Databricks-based cloud data platform. Build scalable batch, streaming, CDC, and API pipelines using Python, SQL, Spark, and Azure; establish engineering standards, governance, observability, and CI/CD practices; develop AI-ready semantic data products; and mentor data engineers. Partner with BI, product, security, governance, and MLOps teams to support analytics, machine learning, generative AI, and self-service reporting.

Designs and optimizes enterprise data solutions, including scalable cloud data pipelines, repositories, dashboards, reporting, and analytics capabilities. Develops SQL and Python-based ETL/ELT processes, enforces data governance and privacy controls, and leads AI/ML model integration and lifecycle management. Partners with stakeholders using Agile practices to deliver technical projects and provide reliable decision-support solutions.

Own end-to-end client data migrations: reverse-engineer legacy ETL, design and build Airflow DAGs and Spark jobs, validate and reconcile data at scale, document architecture and decisions, and coordinate cross-functional teams through go-live and hypercare.

Lead technical strategy for commercial data governance, data risk management, and reliability of data-driven workloads. Provide architecture, design, development, and testing leadership while mentoring engineers and driving technical decisions. Partner with stakeholders to scale governance practices, improve delivery processes, and build trusted data solutions using cloud platforms, Spark, Python, SQL, and data-lakehouse architectures.

Designs, develops, and maintains scalable AWS data pipelines, data lakes, warehouses, and serverless applications. Builds ingestion and ETL/ELT solutions using AWS services, migrates data, implements streaming architectures, and optimizes performance, cost, reliability, and scalability. Participates in architecture, testing, CI/CD, and code reviews while collaborating cross-functionally. Leads high-impact data engineering initiatives and provides organizational and executive guidance.

Design, build, validate, and support reliable data solutions for enterprise operations and analytics. Responsibilities include developing pipelines and transformations, translating business requirements into technical solutions, implementing data quality checks and monitoring, tuning SQL and processing jobs, documenting data definitions and lineage, and supporting security, access controls, reconciliation, and failure recovery. The role collaborates with stakeholders across the delivery lifecycle to ensure accurate, maintainable, and governed data.

Design and build scalable data pipelines, migrations, integrations, curated datasets, semantic models, dashboards, and analytics products. Evolve cloud-native lakehouse platforms, establish data quality and performance practices, and deliver trusted, AI-ready insights. Partner with technical, product, and insurance-domain stakeholders to translate business needs into reliable data solutions. The role requires deep property and casualty insurance expertise, extensive data engineering experience, and ownership of complex technical problems.

Lead production of marine and anadromous species range maps under FWS standards. Aggregate, normalize, transform, and validate spatial data; analyze habitat intersections; coordinate review cycles with FWS and NOAA Fisheries subject matter experts; document quality assurance; and obtain government sign-off for deliverables.

Develop and support enterprise data pipelines, integrations, data models, ETL/ELT processes, and analytics environments. Build Power BI datasets, semantic models, dashboards, and reports for Finance, Sales, Operations, Supply Chain, and other teams. Monitor production data flows, troubleshoot pipeline and reporting issues, implement data-quality checks, and improve governance and architecture. Partner with technical and business stakeholders to deliver scalable solutions and support future AI and automation capabilities.

Analyzes and interprets data to support operational and management decisions. Responsibilities include modifying reports, creating presentations, identifying trends, performing data audits, supporting automation, maintaining databases, producing ad hoc analyses, and implementing data governance and quality requirements. The role collaborates with senior analysts, managers, and the centralized Data Governance team, while potentially guiding junior analysts.

Develop and support enterprise data pipelines, integrations, data models, ETL/ELT processes, quality checks, and analytics environments. Build Power BI datasets, semantic models, dashboards, and reports for Finance, Sales, Operations, and Supply Chain. Troubleshoot data, pipeline, reporting, and performance issues; improve governance and reliability; and help transition Microsoft Fabric ownership from external partners. Partner with technical and business stakeholders to deliver scalable data solutions and support future AI and automation capabilities.

Designs and leads enterprise data pipelines and integrations across Snowflake, Oracle Fusion, on-premises databases, and cloud applications. Ensures data quality, governance, security, scalability, and performance while supporting Power BI, AI/ML, and self-service analytics. Partners with business and technical stakeholders, leads migrations, mentors engineers, optimizes data platforms, and establishes engineering best practices.

Design and operate end-to-end infrastructure for intracortical neural data, from ingestion and validation through preprocessing, versioned dataset releases, and training data loaders. Preserve scientific metadata and provenance while implementing signal processing, quality checks, storage optimization, parallel processing, and monitoring. Collaborate with neuroscience, machine learning, infrastructure, and IT teams to support scalable, reliable model training and sensitive research-data management.

Lead end-to-end data engineering workstreams, building reliable cloud pipelines, governed AI and retrieval workflows, trusted data models, event-driven systems, and automated recovery. Own production reliability, security, observability, cost optimization, testing, audit evidence, and operational readiness. Use Python, advanced SQL, coding agents, machine learning, and defensive AI engineering to deliver secure, resilient platforms while mentoring engineers and translating business needs into practical solutions.

Designs, builds, deploys, and operates scalable data pipelines, datalake systems, streaming and batch processing frameworks, MLOps infrastructure, data documentation tooling, and an agentic data exploration platform. The role improves engineering velocity, data reliability, observability, discoverability, and machine learning development across customer-facing products.

Designs, builds, deploys, and maintains scalable data pipelines, data lake systems, batch and streaming processing, MLOps frameworks, data documentation tooling, and an agentic data exploration platform. The role supports customer-facing products and enables engineering teams through reliable infrastructure, observability, data discovery, and AI capabilities.