opinov8

Architect

• Architect and design enterprise-scale data platforms leveraging Azure Databricks and Azure cloud services;
• Define and implement Lakehouse architecture patterns using Delta Lake and Databricks best practices;
• Design scalable data ingestion, transformation, and serving frameworks supporting both batch and streaming workloads;
• Design and implement real-time and near real-time data processing solutions using Structured Streaming, CDC, Change Data Feed (CDF), and event-driven architectures;
• Design and manage multi-workspace Databricks environments and shared Metastore architectures.
• Establish platform governance, security controls, cluster policies, RBAC models, and access management frameworks;
• Design and implement CI/CD pipelines for Databricks assets, infrastructure, and data workloads.
• Automate infrastructure provisioning using Infrastructure as Code (Terraform, ARM, or Bicep);
• Develop deployment strategies and release management processes across development, staging, and production environments;
• Optimize Databricks workspaces, clusters, jobs, and storage configurations for performance and cost efficiency;
• Implement monitoring, alerting, logging, and observability solutions for platform operations;
• Define and enforce data architecture standards, data quality frameworks, and governance policies;
• Integrate Azure Databricks with Azure Data Factory, Azure Synapse, Azure Storage, Azure Key Vault, Azure Entra ID, and other Azure services;
• Support Lakeflow Connect, managed connectors, CDC implementations, and enterprise data ecosystem interoperability;
• Collaborate with engineering and business teams to translate requirements into scalable technical solutions;
• Provide architectural guidance, technical leadership, and mentorship to engineering teams.

Azure Databricks Architect

Data Engineer

• Actively design, build, and optimize scalable ETL pipelines for structured and semi-structured data supporting Insights use cases, growth metrics, pubsports, and other out-of-game data sets.
• Design and implement complex data models using industry best practices that capture a complete ecosystem view of in- and out-of-game experiences, while ensuring accuracy, scalability, and long-term usability.
• Architect and implement robust, maintainable, and high-performance data solutions.
• Automate workflows to reduce manual intervention and enhance data processing efficiency, including automation for content, growth, and pubsports areas of coverage.
• Mentor data engineers across all levels, through code reviews and technical guidance, setting best practices and fostering technical growth.
• Optimize query performance and resolve pipeline bottlenecks to improve data accessibility.
• Evaluate and prototype new tools, frameworks, and methodologies to advance data engineering capabilities.
• Support cost optimization by ensuring scalable and efficient data solutions.
• Ensure data quality, governance, and compliance with regulatory standards (e.g., GDPR, CCPA).
• Contribute to the Data Engineering team's discipline by shaping infrastructure, craft standards, tooling, and organizational best practices.

Staff Data Engineer – Product Insights, Gaming Industry

• Actively design, build, and optimize scalable ETL pipelines for structured and semi-structured data supporting Insights and Finance use cases that analyze technology usage and costs (e.g. AWS CUR).
• Design and implement complex data models using industry best practices that capture a complete view of technology usage and costs, while ensuring accuracy, scalability, and long-term usability.
• Partner closely with internal stakeholders to understand their needs and craft solutions that drive their ultimate outcomes.
• Architect and implement robust, maintainable, and high-performance data solutions.
• Automate workflows to reduce manual intervention and enhance data processing efficiency, including automation for finance processes.
• Mentor junior data engineers through code reviews and technical guidance, setting best practices and fostering technical growth.
• Optimize query performance and resolve pipeline bottlenecks to improve data accessibility.
• Ensure data quality and governance.

Staff Data Engineer – Technology Strategy and Ops, Gaming Industry

Engineer

• Design, build, and maintain scalable ETL/ELT pipelines using Azure Databricks and Apache Spark;
• Develop and optimize PySpark/Spark SQL notebooks for large-scale data processing;
• Implement batch and real-time data ingestion pipelines using Azure Data Factory (ADF), Databricks Workflows, and Structured Streaming;
• Manage and optimize Delta Lake/Lakehouse architecture, including Bronze, Silver, and Gold layers;
• Integrate data from multiple sources including APIs, databases, cloud storage, and third-party systems;
• Work with Azure services such as ADLS Gen2, Azure Synapse Analytics, Azure SQL Database, and Azure DevOps;
• Monitor, troubleshoot, and improve pipeline performance, scalability, and cost optimization;
• Implement data governance, security, access controls, and compliance standards using Unity Catalog and RBAC;
• Collaborate with Data Engineers, Data Scientists, Analysts, and Product teams to deliver reliable data solutions;
• Support CI/CD implementation, automation, and infrastructure-as-code practices.

Azure Databricks Engineer

Scrum Master

• Own the full delivery cycle: sprint planning, execution, release, and retrospectives
• Proactively identify and remove blockers, escalating with context and solutions
• Use Scrum/Kanban metrics (velocity, cycle time, throughput) to improve delivery flow
• Manage cross-team dependencies without slowing down progress
• Actively use AI tools for reporting, sprint summaries, and process automation
• Introduce and scale AI-driven practices in delivery workflows
• Control scope and manage mid-sprint changes with clear communication
• Translate stakeholder needs into actionable tasks and acceptance criteria
• Foster a high-trust, psychologically safe team environment
• Drive continuous improvement through actionable retrospectives

Agile Project Lead – Healthcare Project

Technical Product Manager

• Own and maintain the migration backlog - keep it prioritized, refined, and ready so the team always knows what's next and why;
• Run refinement sessions that actually refine - break down work, clarify scope, resolve ambiguity before it reaches development;
• Be the main point of contact for customers during migration - understand their data, their systems, and their concerns, and translate that into clear requirements for the team;
• Ask the right technical questions - you don't need to write the code, but you need to understand how APIs work, what a data mapping looks like, and where integrations tend to break;
• Work with engineering to scope migration paths - what data moves, how it moves, what needs transformation, and what gets left behind;
• Spot risks early - data quality issues, missing fields, incompatible formats - and make sure the team addresses them before they become blockers;
• Coordinate across teams when migrations touch other parts of the platform;
• Track migration progress and communicate clearly to customers and internal stakeholders.

Azure Databricks Architect

Staff Data Engineer – Product Insights, Gaming Industry

Staff Data Engineer – Technology Strategy and Ops, Gaming Industry

Azure Databricks Engineer

Agile Project Lead – Healthcare Project

Technical Product Owner – Healthcare