Data Engineer

• Desenvolver, manter e evoluir soluções de engenharia de dados, garantindo qualidade, disponibilidade e performance dos dados.
• Construir e otimizar processos de ingestão, transformação e disponibilização de dados.
• Desenvolver e manter rotinas utilizando SQL e linguagens de programação aplicadas ao contexto de dados.
• Atuar na modelagem de dados e na implementação de boas práticas de desenvolvimento.
• Utilizar ferramentas de versionamento de código, realizando controle e gestão de alterações em ambientes colaborativos.
• Desenvolver e manter pipelines de CI/CD para automação de processos de entrega.
• Trabalhar com serviços AWS, especialmente Amazon Redshift e Amazon S3, apoiando a construção e sustentação das soluções de dados.
• Participar de discussões técnicas, análises de requisitos e definição de soluções alinhadas às necessidades do negócio.
• Monitorar e atuar na melhoria contínua da performance e confiabilidade dos ambientes de dados.
• Híbrido (1x por semana presencial na Faria Lima para quem reside em São Paulo).

Engenheiro de Dados – Pl.

• Design, develop, and optimize scalable data pipelines (batch and real-time) that power unified customer data and insights
• Partner closely with analytics, data science, and business stakeholders to deliver high-quality, reusable, and governed data models that support the CDP and broader customer data products
• Build and manage robust ETL/ELT data workflows, ensuring reliable ingestion, transformation, and integration of customer data from diverse sources
• Establish and enforce data quality, governance, privacy, and security standards, with a focus on trusted and compliant customer data
• Drive the evolution of the customer data architecture, improving platform reliability, scalability, and performance to support growing data volumes and use cases
• Contribute to the design and implementation of identity resolution, data unification, and customer 360 capabilities within the CDP ecosystem
• Mentor junior engineers and promote engineering best practices, data standards, and operational excellence
• Support and enhance real-time and streaming data pipelines to enable timely customer insights and activation use cases

Senior Data Engineer, Digital Technology

• Design, build, and maintain scalable batch and streaming data pipelines supporting enterprise analytics, reporting, and downstream consumption
• Develop and optimize data ingestion, transformation, and orchestration workflows across structured and semi‑structured data sources
• Engineer and maintain curated, analytics‑ready data models (e.g., dimensional, canonical, or domain‑oriented datasets)
• Ensure data solutions meet performance, reliability, availability, and recoverability expectations
• Implement data solutions aligned to Penn Mutual’s cloud data platform strategy , including cloud storage, compute, and analytics services
• Apply data architecture patterns that support data lakes, lake houses, and analytical warehouses
• Partner with Enterprise Architecture to ensure data solutions conform to technology standards, integration patterns, and security requirements
• Contribute to platform evolution decisions, including tooling selection, architectural patterns, and modernization initiatives
• Embed data quality checks, validation rules, and observability into pipelines to ensure trusted data
• Support data governance and stewardship practices , including metadata management, lineage, and controlled data access
• Ensure data solutions comply with security, privacy, and regulatory requirements relevant to financial services and insurance
• Collaborate with analytics, reporting, and data science teams to enable self‑service analytics and advanced insights
• Translate business requirements into well‑designed data structures and datasets that are easy to consume and reuse
• Support downstream use cases including dashboards, regulatory reporting, operational analytics, and advanced modeling
• Promote engineering best practices including version control, automated testing, CI/CD, and documentation
• Drive continuous improvement through evaluation of emerging data technologies and industry trends

Senior Data Engineer

• Lead the design and implementation of scalable, high-performance data architectures
• Architect and build data pipelines for processing and analyzing large-scale datasets
• Mentor and guide junior data engineers in best practices
• Ensure reliability, efficiency, and security of data pipelines
• Collaborate with cross-functional teams to align data solutions with business needs
• Lead the adoption of new technologies and tools that enhance data engineering capabilities
• Oversee the development of data models, schemas, and data marts
• Implement data governance frameworks including data lineage and quality standards

• Develop and maintain SQL code and SSIS packages.
• Analyze data and solve new and existing business issues.
• Reviewing query performance and optimizing code.
• Provide production level support.
• Fully document all processes that are being created.

Prod Support Data Engineer, T-SQL

• Design, implement, and continuously improve systems for data ingestion, processing, storage, and sharing
• Build and optimize data architectures for performance, scalability, and reliability
• Develop and maintain ETL/ELT pipelines using modern tools and frameworks
• Ensure seamless integration and synchronization across systems
• Uphold high standards of data quality, security, availability, and performance
• Collaborate with analysts, software engineers, and business stakeholders to understand data needs and deliver solutions
• Perform code reviews, troubleshoot software, and fix defects
• Implement monitoring and alerting for data workflows
• Gain expertise in a variety of banking processes and products

• Define and own the roadmap for internal data products, including data governance frameworks, data categorization and taxonomy, and CDP and Single Customer View capabilities
• Translate business needs into scalable data solutions across Product, Marketing, Fraud, Finance, and Operations while partnering with Data Architecture to align product priorities with long-term platform strategy
• Ability to embed data governance requirements into the product development lifecycle: Standardized event tracking and instrumentation, schema design, versioning, and documentation, and PII classification and consent alignment
• Establish and communicate clear data ownership and accountability across teams
• Partner with engineering teams to ensure all new features meet data quality, compliance, and observability standards while driving consistency in event tracking across mobile, web, and backend systems
• Standardize and improve data categorization across Kafka, Snowflake, BigQuery, and downstream systems
• Collaborate with Data Engineering to ensure datasets are clearly defined and documented, easily discoverable via catalogs, and mapped to business use cases
• Reduce duplication and fragmentation across data sources
• Own product requirements for Customer Data Platform and Single Customer View development, including: Identity resolution and profile stitching, behavioral, transactional, and profile unification, segmentation and activation capabilities
• Enable real-time and batch use cases such as: Personalization and recommendations, player lifecycle management, alerts and triggers across product and marketing systems
• Lead and inspire the adoption of CDP capabilities across the organization
• Lead the development of AI-powered data tools that simplify access to insights: Natural language querying, automated insights and reporting, and intelligent alerting and anomaly detection
• Partner with BI, Data Science, and Engineering to prioritize high-impact AI use cases
• Be a conduit between technical data teams and business stakeholders
• Ensure data products are intuitive, accessible, and widely adopted
• Develop training, documentation, and change management programs to support team members across Caesars Digital
• Continuously prioritize based on measurable business outcomes

Senior Product Manager – Data Platform

• Responsible for leading the modernization, optimization, and stabilization of the Wisconsin Medicaid Market's data platform ecosystem.
• Own the market's Data Warehouse and ODS, drives ETL/data movement strategy (including SSIS modernization).
• Partners closely with the Market BI team to improve data access and flow.
• Owns and evolves the Wisconsin Medicaid Market's data stores and data movement ecosystem, including the Data Warehouse and ODS.
• Accountable for modernizing and optimizing the market's data platform to improve reliability, reduce technical debt, strengthen observability/fault tolerance, and increase engineering efficiency.

Lead Data Engineer – Modernization, Reliability

• Design, build, and maintain scalable streaming data architectures using Kafka, MSK, and Kinesis
• Develop real-time data pipelines that handle high-volume, high-velocity data streams
• Implement event-driven architectures and microservices patterns for streaming data processing
• Create and optimize data streaming topologies for complex event processing scenarios
• Design fault-tolerant streaming systems with proper error handling and data recovery mechanisms
• Configure, deploy, and manage Apache Kafka clusters and AWS MSK environments
• Implement Kafka Connect pipelines for streaming data integration
• Design optimal Kafka topic partitioning strategies and replication configurations
• Monitor and optimize Kafka cluster performance, throughput, and latency
• Implement Kafka security configurations including SSL/TLS, SASL, and ACLs
• Manage Kafka Schema Registry for data serialization and evolution
• Design and implement Amazon Kinesis Data Streams and Kinesis Data Firehose solutions
• Configure Kinesis Analytics applications for real-time stream processing
• Optimize Kinesis shard management and auto-scaling configurations
• Implement Kinesis data retention and archival strategies
• Integrate Kinesis with other AWS services for comprehensive streaming solutions
• Develop real-time stream processing applications using Apache Spark Streaming, Kafka Streams, or AWS Lambda
• Implement complex event processing (CEP) patterns for real-time analytics
• Build streaming ETL pipelines that transform data in motion
• Create real-time aggregations, windowing operations, and stateful stream processing
• Optimize streaming query performance and resource utilization
• Ensure seamless integration between streaming systems and data lakes, data warehouses, and operational databases
• Implement data lineage and monitoring for streaming data pipelines
• Create automated data quality checks and validation for streaming data
• Manage data serialization formats (Avro, JSON, Protobuf) and schema evolution
• Coordinate with data scientists and analysts to ensure streaming data meets analytical requirements
• Implement Infrastructure as Code (IaC) for streaming data platforms using Terraform or CloudFormation
• Automate deployment and management of streaming infrastructure through CI/CD pipelines
• Monitor streaming system health, performance metrics, and alerting
• Implement disaster recovery and high availability strategies for streaming systems
• Stay current with emerging trends in streaming technologies and cloud-native solutions
• Collaborate with data architects, data scientists, and application teams on streaming data requirements
• Support rigorous project governance through daily progress reviews and time tracking
• Provide technical leadership and mentorship to junior data engineers
• Communicate complex streaming concepts to technical and non-technical stakeholders
• Operate with transparency and responsiveness to support high-performing teams.

• Define the target architecture for customer, account, contact, lead, opportunity, activity, campaign, and support data.
• Assess and improve Salesforce data across the lead-to-support lifecycle.
• Design canonical data models, entity relationships, identity-resolution rules, and system-of-record definitions.
• Lead data-cleansing and remediation initiatives, including deduplication, normalization, enrichment, validation, and historical cleanup.
• Establish matching, merging, and survivorship rules for people, companies, accounts, and related records.
• Architect integrations between Salesforce, data warehouses, operational systems, support platforms, and third-party data providers.
• Define standards for field definitions, lifecycle stages, ownership, metadata, lineage, retention, and access controls.
• Implement automated monitoring for data quality, completeness, freshness, consistency, and integration failures.
• Improve the flow of data between marketing, sales, customer success, and support systems.
• Evaluate third-party data sources and define how external data should be matched, validated, and incorporated into enterprise systems.
• Partner with Business Systems, Revenue Operations, Data Engineering, Analytics, Security, and business stakeholders to translate operational requirements into durable technical solutions.
• Produce architecture diagrams, data dictionaries, integration specifications, governance documentation, and implementation guidance.
• Provide technical leadership and guide teams through complex data architecture and system-design decisions.
• Support and improve integrations involving Salesforce and go-to-market data platforms such as Clay, PitchBook, ZoomInfo, HG Insights, Cognism, Harmonic, and Meticulate.

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Engenheiro de Dados – Pl.

Senior Data Engineer, Digital Technology

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