Top Data & Analytics Jobs

Staff Data Engineer responsible for setting technical direction, architecting scalable data platforms, leading cross-functional initiatives, improving reliability and performance, and enabling AI-assisted analytics and development. The role includes hands-on development with Python, SQL, cloud data warehouses, orchestration, infrastructure as code, Kubernetes, Kafka, and streaming pipelines, along with mentoring, technical advocacy, documentation, code reviews, and on-call troubleshooting.

Entry-level rotational Data Engineer responsible for SQL development, SSIS data transformations, data validation, testing, performance tuning, data profiling, and reporting solutions. The role collaborates with IT, project managers, analysts, and business stakeholders to implement enterprise data solutions across Salesforce, Marketing Cloud, and other platforms. This is a 100% on-site position in San Diego, California.

Designs and supports scalable data engineering and authorization platforms for brokerage technology. Responsibilities include cloud database development, data modeling, SQL and stored procedures, CDC-based real-time pipelines, Kafka integrations, data migration, performance tuning, monitoring, incident management, root cause analysis, and production support for highly available Tier 0 systems.

Designs and leads enterprise data engineering solutions, including cloud data pipelines, repositories, models, ETL/ELT processes, reporting platforms, and data governance. Integrates and operationalizes AI and machine learning models, establishes MLOps lifecycle controls, and ensures data quality, security, lineage, and compliance. Translates stakeholder needs into technical requirements, leads projects, mentors staff, and supports analytics capabilities across the organization.

Build and maintain scalable data pipelines, platforms, and ETL/ELT workflows that organize structured and unstructured data. Develop scripts and data feeds, manipulate Elastic data, support reporting and analytics, and convert varied data into usable formats. Collaborate with analysts, engineers, developers, and data consumers in an agile environment while assessing, designing, monitoring, and operating data platforms for mission-driven clients.

Build and own scalable data pipelines using Python, dbt, Airflow, and Snowflake. Design data architecture, optimize performance, implement data quality and observability, troubleshoot production issues, and process diverse data sources. Support executive reporting and ad hoc analysis, contribute to Tableau visualization, collaborate with engineering and business stakeholders, and mentor teammates through reviews and knowledge sharing.

Lead the technical strategy and architecture for a modern, real-time enterprise data platform. Design scalable data models, ingestion strategies, governance, security, compliance, and cost-optimization initiatives. Drive the data roadmap across multiple squads, make strategic technical decisions, collaborate with business stakeholders, troubleshoot complex issues, and mentor engineers. The role requires hands-on leadership in data architecture, real-time platforms, and enterprise data strategy.

Designs, builds, operates, and optimizes data platforms, pipelines, and products supporting business and operational processes. Integrates manufacturing, quality, laboratory, MES, LIMS, ERP, and other systems into enterprise analytics platforms. Develops Azure Synapse, Azure Data Factory, and Microsoft Fabric solutions, monitors data quality, resolves integration issues, documents solutions, and partners with stakeholders across the US and Germany.

Designs, develops, and maintains scalable end-to-end data pipelines and architectures for the National Guard Bureau’s enterprise data environment. Responsibilities include ETL development, data modeling, cleansing, integration, quality assurance, governance, large-scale data processing, troubleshooting, and collaboration with analysts, data scientists, and stakeholders. The role requires expertise in Databricks, Python, Spark, Scala, JavaScript/JSON, SQL, and related big-data technologies.

Data Engineer II will modernize BlueLabs’ data platform by leading Kimball dimensional modeling and legacy warehouse migrations, building reliable production data pipelines, and enforcing data quality standards. The role requires advanced SQL, Python, and dbt expertise, hands-on Snowflake and Redshift experience, and ownership of testing, documentation, observability, and scalable architecture. The engineer will collaborate cross-functionally, guide Data Engineer I team members, contribute to architectural decisions, and translate business logic into maintainable, cloud-agnostic data layers.

Build and maintain data pipelines, transformations, integrations, and workflow orchestration using Snowflake, SQL, Python, Airflow, and related tools. Support data quality, governance, metadata, documentation, troubleshooting, production operations, and API integrations. Collaborate with product, engineering, analytics, and business teams in an Agile environment while developing reliable, reusable data solutions.

Build and maintain production-grade data pipelines, connectors, and harmonized snapshots across clinical studies and applications. The role covers ingestion, transformation, orchestration, data modeling, quality controls, monitoring, lineage, auditability, regulatory compliance, CI/CD, and integration with Databricks and Snowflake environments. The engineer will validate source data, manage schema changes, optimize performance, and collaborate with platform and AI engineers to support reliable data and AI applications.

Leads the design, development, and operation of Astrana’s enterprise data platform. Builds scalable Databricks, Spark, PySpark, SQL, and Delta Lake pipelines; integrates healthcare data; develops unified data models and APIs; supports Lakebase and PostgreSQL applications; and implements governance, data quality, monitoring, CI/CD, automation, and performance optimization. The role also handles incidents, disaster recovery coordination, engineering standards, code reviews, and mentoring across internal and vendor teams.

Build and deploy scalable data pipelines and platforms that organize structured and unstructured data for mission-driven client projects. Collaborate with analysts, developers, data engineers, and data consumers in an agile environment. Support data assessment, exploration, examination, platform design, development, and maintenance. The role requires Palantir Foundry, Python, CI/CD, SQL, TS/SCI clearance, and travel to OCONUS locations up to 25% of the time.

Designs, builds, and maintains scalable AWS and on-premises data pipelines supporting genomic data science models. Responsibilities include API ingestion, storage, transformation, data modeling, workflow development, troubleshooting, documentation, infrastructure-as-code, and cross-functional collaboration. The role requires hands-on AWS pipeline development, HPC experience, container orchestration, Python and SQL expertise, CI/CD, secure access management, and production data engineering leadership in a consulting environment.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Supports and improves critical data pipelines and infrastructure through troubleshooting, monitoring, data quality investigations, query optimization, and incident management. Designs batch and real-time pipelines, automates operational workflows, administers tool access, documents solutions, and mentors support engineers. Provides on-call coverage and coordinates issue resolution across teams and time zones while applying best practices for data warehousing, ETL/ELT, orchestration, and reliability.

Build and maintain dbt models and data pipelines feeding Snowflake from agency, CRM, HR, finance, and other sources. Onboard data sources, improve testing and observability, troubleshoot nightly runs, optimize Snowflake performance and costs, and contribute to governance controls. Develop backend data layers for internal applications, support CI pipelines, and collaborate with analysts and business stakeholders to deliver reliable, documented data models.

Develop data algorithms and systems to analyze structured and unstructured information for defense and intelligence clients. Gather, clean, parse, and interpret disparate data sources using statistical programming and scientific methods. Translate complex data into objective insights supporting military infrastructure, resource analysis, planning, and policy decisions. Collaborate with clients and team members in a fast-paced environment while maintaining required DoD clearance.

Build and maintain scalable data pipelines, models, cost allocation systems, metrics, dashboards, and analytical tools for PlayStation’s global infrastructure. Connect infrastructure, financial, engineering, and product data to analyze cost, utilization, capacity, and unit economics. Ensure data quality, testing, observability, documentation, and reproducibility while partnering across engineering, product, finance, and operations teams.

Build trusted, secure, and repeatable data pipelines, transformations, data products, retrieval layers, and semantic data assets. Assess data quality, lineage, governance, privacy, and access constraints; support AI retrieval, embeddings, vector stores, and grounding strategies. Create test and de-identified datasets, implement monitoring and quality checks, document data assets, and ensure reliable data for AI products, control-group testing, and production handoff.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and ETL/ELT workflows. Build trusted data products for analytics, automation, and AI agents; translate ambiguous business needs into scalable solutions; lead cross-team initiatives; ensure reliability and data quality; mentor engineers and analysts; and develop, evaluate, and monitor production LLM and agent systems using Python and SQL.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, ensure data quality and reliability, and mentor engineers and analysts. Build, evaluate, and monitor production LLM and agent systems, automate marketing workflows with AI, and deliver high-quality Python and SQL solutions supporting Marketing, Sales, and R&D.

Supports and modernizes enterprise data services, including SQL Server and cloud-based ETL/ELT, data warehouses, pipelines, data integration, dimensional modeling, and OLTP environments. Develops SSIS, Azure Data Factory, and Microsoft Fabric pipelines; troubleshoots production data and integration issues; optimizes SQL and stored procedures; documents data flows and lineage; and collaborates with technical and business teams on secure, compliant data solutions.

Design, develop, and maintain data pipelines, ETL processes, integrations, data infrastructure, and complex data models. Build and support data warehouses and analytics solutions using SQL, PL/SQL, Oracle, Snowflake, and cloud technologies. Collaborate with analysts, architects, developers, data scientists, and business stakeholders throughout the delivery lifecycle. Provide production support, troubleshoot issues, tune database performance, and contribute to data-driven reporting and AI tools.