The Director of Data Engineering will lead and scale the Data Engineering and Analytics Engineering functions, owning the post-application data platform and its architecture, governance, and operating model. This foundational leadership role ensures best in class engineering standards, helps drive the organization toward a durable, auditable, data-product operating model, and modernizes and expands a Snowflake-centered data stack to include building and operating scalable distributed data processing and real-time streaming systems that support event-driven data products for operational use cases.

The role partners closely with Product, Software Engineering, Data Product Managers, Marketing, Compliance, Finance and Data Science to ensure our data ecosystem is production-grade, scalable, and compliant in a regulated gaming environment.

What You'll Do:

Team Leadership & Organizational Growth

• Lead and grow Data Engineering and Analytics Engineering teams; recruit, mentor, and elevate technical capabilities around distributed data processing, stream processing, and event-driven architectures
• Define ownership models for platform components and data products; set performance and hiring plans.
• Advance engineering standards for deployment: development, testing, documentation, release management, and SLAs.
• Partner with Data Product Managers to define data assets as versioned, documented data products with clear ownership and SLAs.

Platform, Architecture & Engineering Excellence

• Own the administration, reliability, governance, and strategic expansion of the Snowflake-based post-application data platform to operate scalable distributed data processing pipelines capable of handling high-volume event data across both batch and streaming workloads.
• Manage platform governance, access control, security, cost, and capacity planning.
• Standardize DBT deployment methods, promotion workflows, and environment management.
• Implement Infrastructure-as-Code (Terraform or similar) for reproducible, auditable infrastructure.
• Deliver monitoring, observability, lineage, and data quality frameworks; enforce traceability and auditability for regulated reporting.
• Own and evolve ingestion and warehouse architecture and transformation frameworks .
• Improve performance, cost efficiency, reliability, and scalability of data transformation and storage layers.
• Define and enforce engineering standards across the data lifecycle and establish SLAs and reliability metrics for critical assets - ensuring all pipelines, transformations, and reporting are auditable and documented for internal and external review

Event-Driven Architecture & Player 360

• Partner with Software Engineering on event messaging, streaming architecture, and ingestion patterns (built on technologies such as Kafka, Kinesis, Spark Structured Streaming, Flink, or equivalent platforms).
• Lead technical design and development of a unified Player 360 data foundation that integrates transactional, behavioral, and event-level data.
• Align application schema design with downstream analytical, product, and regulatory requirements.
• Balance real-time and batch processing strategies according to business needs and cost/complexity tradeoffs.

What You'll Bring:

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related STEM field preferred. Equivalent practical experience will be considered.
• 8+ years of experience in data engineering, with 3+ years of experience in leadership roles. 
• Experience transitioning an organization from private/startup to public company.
• Experience in regulated industries (gaming, fintech, payments, healthcare).
• Prior experience building Player 360 / Customer 360 platforms.
• Deep hands-on experience with Terraform / IaC and production-grade streaming systems (Kafka, Kinesis, etc.).
• Familiarity with GenAI governance frameworks and scalable AI integration patterns such as Harness Engineering.
• Able to travel occasionally both domestically and internationally. #LI-REMOTE #LI-PROMOTE

The Analytics Engineering Technical Lead will define and own the modeling architecture, standards, and technical direction within RSI’s cloud-native Data Platform. This role is accountable for ensuring that analytics-ready data is reliable, scalable, and aligned to long-term business needs across growth, operations, and risk.

You will lead the design and governance of core data models in Snowflake and dbt, establishing best practices that enable high-quality, self-service analytics without sacrificing trust or performance. As a hands-on technical leader, you will set standards for modeling, testing, documentation, and deployment while mentoring other Analytics Engineers and raising the overall technical bar of the team.

This role partners closely with Data Engineering, Analytics, and business stakeholders to translate evolving requirements into durable data products. You will help balance speed and governance, prioritize platform investments, and ensure our data foundation scales with the business.

Our modern data stack includes Snowflake, AWS, Python, Airflow, and dbt.

What You'll Do:

• Lead the design, development, and lifecycle management of curated datasets, dbt models, and analytics data marts aligned to business domains and metrics.
• Establish and evolve standards and best practices for dbt modeling, testing, documentation, and code organization to support long-term scalability.
• Ensure analytical datasets are accurate, reliable, and trusted, serving as a single source of truth for business, product, and stakeholders.
• Embed data quality checks, documentation, and governance directly into analytics pipelines.
• Partner closely with Product, Data Engineering and Analytics teams to translate business requirements into well-modeled, performant data products.
• Mentor and support Analytics Engineers through technical guidance, design reviews, and knowledge sharing.
• Continuously improve and modernize analytics workflows by introducing automation, tooling enhancements, and AI-assisted development where appropriate.
• This role includes technical leadership and mentoring responsibilities for the team, including other Analytics Engineers.

What You'll Bring:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related quantitative discipline.
• Technical Leadership: Proven experience defining technical direction, standards, and architectural decisions within analytics engineering or curation teams.
• Data Modeling Expertise: Deep understanding of dbt, data marts, dimensional modeling, and self-service BI enablement.
• Data Engineering Background: 7+ years of experience in data warehousing or development; 5+ years of experience in hands-on data engineering using SQL and Python.
• Cloud Infrastructure: Proficiency with cloud platforms (AWS, GCP, or Azure), including containerization (Docker/Kubernetes).
• Tooling & Orchestration: Experience with tools like dbt, Airflow, S3, REST APIs, and large-scale DWHs (Snowflake, Redshift, BigQuery, etc.).
• Change Management: Skilled at leading cross-functional data initiatives and driving adoption of new tools and standards.
• People Leadership: Experience mentoring and guiding distributed teams across time zones.
• Communication: Strong written and verbal English skills with the ability to collaborate across technical and business functions.
• Able to travel occasionally both domestically and internationally #LI-HYBRID

❗️Please note that this role is office based for Valencia, Spain (RAS Valencia - Servicios Profesionales de Recursos Humanos).

❗️We can provide relocation assistance if you're outside of the city or country.

We’re looking for a highly skilled Data Architect with experience in data warehousing to design and optimize our data platform. You’ll play a critical role in shaping our data ecosystem—defining architecture, ensuring scalability, and enabling advanced analytics and reporting across the organization.

Key Responsibilities

• Design and implement scalable, secure, and high-performance data architectures on AWS using Snowflake as the central data warehouse;
• Design and optimize ELT/ETL pipelines using tools such as Airflow and Snowflake Streams and Procedures;
• Lead the design of data models (Star/Snowflake schema) and data marts for analytics and operational reporting;
• Work with data engineers, BI, and other technical teams and business stakeholders to understand requirements and translate them into technical data solutions;
• Establish data governance, cataloging, and lineage practices;
• Ensure compliance with data security, privacy, and regulatory standards;
• Drive performance tuning, cost optimization, and best practices in Snowflake and across the data stack;
• Document architectural decisions and technical designs for team collaboration and future scalability.

Requirements

• 4+ years of experience in data engineering or architecture roles;
• Strong experience with AWS cloud services such as S3, Redshift, Glue, Lambda, IAM, and CloudFormation/Terraform;
• Proficiency in SQL, Python, and modern data pipeline frameworks (e.g., dbt, Airflow);
• Solid understanding of data modeling, data warehousing principles, and dimensional design;
• Experience with CI/CD practices and infrastructure as code in a data environment;
• Excellent communication and documentation skills.

Nice to Have

• Hands-on experience with Snowflake (warehousing, performance tuning, data sharing);
• Experience integrating with BI tools like Power BI or Tableau;
• Knowledge of data lake architecture and streaming technologies (Kafka, Kinesis);
• Certifications in Snowflake or AWS (e.g., SnowPro Core, AWS Data Analytics Specialty).

✅ Why you should join us?