Top Data & Analytics Jobs

Build and maintain Python and Databricks workflows for data cataloging, API ingestion, and transformation across varied sources and file types. Develop authentication workbooks and Power Apps using Microsoft Power Platform. Gather client requirements, translate technical findings for diverse audiences, and deliver mission-focused data solutions. The role also supports data engineering, metadata management, Agile practices, and federal client initiatives.

Develop and maintain data pipelines and BI solutions for smart factory initiatives. Design data models, document data warehouses, build KPI dashboards, support ad-hoc analysis, and collaborate with stakeholders to enable data-driven decisions.

Architect, build, and operate scalable data platforms processing billions of daily events through real-time and batch pipelines. Own production reliability, data quality, security, observability, and performance while partnering with engineering, analytics, data science, growth, and business teams. Develop frameworks and datasets, make architectural decisions, evaluate technologies, mentor engineers, and help define the platform roadmap.

Supports actuarial data workflows by updating and testing SQL queries, comparing legacy and updated results, performing data quality checks, and documenting data sources and business rules. Partners with actuaries, data scientists, and other data professionals on process improvements while developing technical and communication skills through mentorship.

Lead data engineering initiatives supporting analytics, machine learning, and business intelligence teams. Design complex data warehouse and data lake models, manage large-scale SQL and Python ETL/ELT pipelines, maintain data quality and lifecycle standards, and translate strategic priorities into actionable projects. Provide technical leadership, mentorship, architectural guidance, project planning, and cross-functional collaboration with product, infrastructure, and architecture teams.

Design, develop, and maintain scalable data pipelines using Databricks, Spark, Python, Scala, and Hadoop. Build and optimize ETL/ELT processes, manage batch workflows with Control-M, troubleshoot failures, and implement data validation and quality checks. Support production deployments, performance tuning, incident resolution, and ongoing maintenance while collaborating with architects, analysts, and cross-functional teams.

Develop scalable data engineering solutions for Fidelity’s asset management technology platform. Responsibilities include designing data models and ETL pipelines, developing Oracle PL/SQL and SQL solutions, tuning database performance, supporting Snowflake data warehouses, and applying DevOps and Agile practices. The role also provides production support through an after-hours on-call rotation, including troubleshooting and incident response.

Lead the design and delivery of scalable batch and real-time data pipelines across AWS, Snowflake, PostgreSQL, MongoDB, dbt, and Kafka. Establish standards for data modeling, governance, security, testing, observability, and reliability. Build trusted datasets and semantic layers for BI, analytics, AI/ML, RAG, and LLM applications. Drive platform optimization, data integration, MDM, technical strategy, mentoring, and cross-functional collaboration.

Leads the design, development, governance, and operational support of enterprise data engineering solutions for Fresh Meats analytics. Builds cloud-based data lakes, pipelines, models, integrations, and reporting-ready datasets; implements security, data quality, lineage, observability, and governance controls; supports production incidents and releases; and partners with business, platform, and governance teams. Mentors engineers and analysts while ensuring scalable, documented, secure, and reliable data products.

Designs and maintains scalable data pipelines, data platforms, reporting infrastructure, reconciliation systems, and self-service tools. Develops customer-facing and internal applications, integrates external data, improves data quality and auditability, and optimizes near-real-time processing. The role requires collaboration with engineering, analytics, product, data science, and security teams, along with participation in on-call support and technology evaluation.

Build and maintain scalable data pipelines, data platforms, reporting infrastructure, reconciliation systems, and customer-facing applications. Integrate internal and external data sources, optimize near-real-time processing, ensure data quality and auditability, and develop self-service tools for data consumers. The role also involves researching technologies, supporting blockchain reporting, collaborating across engineering and product teams, and participating in on-call rotations.

Own end-to-end data initiatives, including scalable ingestion and transformation pipelines, reusable data models, orchestration, validation, monitoring, and production operations. Improve performance, reliability, and infrastructure costs while partnering with product, operations, platform, ML, and engineering teams. Establish data engineering standards through technical design, code reviews, documentation, mentorship, and strong data quality and security practices.

Design and scale event-driven data platforms using Apache Kafka and Temporal. Build batch and streaming ETL/ELT pipelines with Python, SQL, and Spark; develop data schemas, validation, contracts, and quality controls; optimize warehouse and lakehouse models; and implement reliable, observable workflows. Collaborate with software, machine learning, and analytics teams on distributed systems, CI/CD, and production data governance.

Build and maintain Snowflake data infrastructure using dbt, including scalable data models, dimensional models, data marts, and ELT/ETL pipelines. Implement data quality, governance, lineage, documentation, and performance optimization practices. Collaborate with analysts and business stakeholders to translate requirements into reliable analytics solutions while applying software engineering practices such as testing, version control, peer review, and CI/CD.

Lead Databricks engineering efforts for enterprise-scale data and analytics platforms in hybrid cloud environments. Design, develop, integrate, and maintain data solutions; resolve technical issues; guide architecture and engineering teams; ensure data quality and observability; coordinate client installations; manage expectations; contribute to product roadmaps; and support deployment, maintenance, modernization, and troubleshooting at customer and Navy sites.

Design and maintain scalable cloud data pipelines, ETL processes, and data warehouse solutions supporting insurance actuarial pricing, reserving, and reporting. Lead adoption of AI, Generative AI, Agentic AI, RAG, automation, and modern cloud data technologies. Collaborate directly with actuarial customers, reconcile data, define technical solutions, mentor engineers, and modernize on-premises pipelines for cloud environments.

Lead the architecture and development of scalable data pipelines and infrastructure. Clean, standardize, and model construction data; build tools for data quality and performance; design integration flows with product, engineering, and customers; apply Python, SQL, and ETL practices; and use AI/ML to improve data classification and mapping. Establish data governance and reliability standards in a high-ownership startup environment.

Designs, develops, tests, and maintains data engineering processes, including data models, mappings, ETL pipelines, metadata structures, data quality rules, and data profiling. Analyzes source and target data, supports analytical and data science outcomes, evaluates downstream impacts, and recommends solutions. The role also requires automation, data governance, and post-deployment support using agile methodologies.

Design and lead scalable, secure, highly available database infrastructure for multi-terabyte SQL Server environments. Set technical direction for data architecture, performance, real-time pipelines, security, and growth. Troubleshoot complex production issues, optimize queries and indexes, guide cross-functional projects, mentor engineers, review changes, reduce technical debt, and evaluate emerging technologies. Monitor database health using Splunk, Datadog, and Grafana while influencing engineering and Site Reliability strategy.

Build and operate a large-scale enterprise data platform spanning batch and streaming processing, lakehouse storage, Kafka infrastructure, APIs, observability, and agentic MCP interfaces. Develop reusable ingestion and data-quality frameworks, improve scalability and resilience, establish engineering practices, support production operations, and mentor engineers. The role requires deep ownership of distributed data infrastructure and practical use of AI coding agents.

Design and operate production-grade data pipelines, backend services, APIs, and agentic AI workflows. Build scalable systems that ingest, transform, and operationalize data for customer-facing and internal applications. Translate ambiguous operational workflows into reliable architectures, improve service observability and performance, and own technical decisions across data modeling, system design, security, and compliance.

Design and develop scalable data pipelines, data models, schemas, and integrations for a video advertising platform. Collaborate across engineering, define user stories, deliver features using AI-assisted tools, write unit tests, maintain code quality, and support production systems on a rotating schedule. The role requires strong SQL, ETL/ELT, dbt, Airflow, Snowflake, AWS, and data architecture expertise.

Leads the enterprise data architecture strategy, designing scalable platforms for secure data exchange, onboarding, discovery, and consumption. Establishes architecture standards, roadmaps, and engineering practices while adopting modern data and AI technologies. Partners with executives and global engineering teams, influences technical decisions, ensures governance and compliance, and mentors teams. The role requires deep expertise in large-scale data platforms, distributed systems, cloud and on-premises environments, and enterprise integration.

Design and build maintainable data pipelines, ETL processes, and data architecture standards. Collaborate with product managers, analysts, and machine learning engineers to translate requirements into automated, scalable solutions. Apply software engineering best practices, asynchronous systems, APIs, streaming, testing, monitoring, and Python and SQL expertise. Help prioritize data initiatives based on business impact and communicate complex technical concepts to technical and non-technical stakeholders.

Design, build, and scale reliable data infrastructure, including event ingestion, stream processing, data warehouses, ETL pipelines, analytics tools, and machine learning systems. Optimize performance, reliability, security, and data quality across AWS regions and data centers. Collaborate with analytics and data science teams, troubleshoot complex systems, influence architecture, conduct code reviews, and mentor data engineers while helping establish engineering best practices.