דרושים » ניהול ביניים » Senior Data Platform Engineer - Entain

This role has been designed as Hybrid with an expectation that you will work on average 2 days per week from an HPE office.
Job Description:
We are looking for a highly skilled Senior Data Engineer with strong architectural expertise to design and evolve our next-generation data platform. You will define the technical vision, build scalable and reliable data systems, and guide the long-term architecture that powers analytics, operational decision-making, and data-driven products across the organization.
This role is both strategic and hands-on. You will evaluate modern data technologies, define engineering best practices, and lead the implementation of robust, high-performance data solutionsincluding the design, build, and lifecycle management of data pipelines that support batch, streaming, and near-real-time workloads.
What Youll Do
Architecture & Strategy
Own the architecture of our data platform, ensuring scalability, performance, reliability, and security.
Define standards and best practices for data modeling, transformation, orchestration, governance, and lifecycle management.
Evaluate and integrate modern data technologies and frameworks that align with our long-term platform strategy.
Collaborate with engineering and product leadership to shape the technical roadmap.
Engineering & Delivery
Design, build, and manage scalable, resilient data pipelines for batch, streaming, and event-driven workloads.
Develop clean, high-quality data models and schemas to support analytics, BI, operational systems, and ML workflows.
Implement data quality, lineage, observability, and automated testing frameworks.
Build ingestion patterns for APIs, event streams, files, and third-party data sources.
Optimize compute, storage, and transformation layers for performance and cost efficiency.
Leadership & Collaboration
Serve as a senior technical leader and mentor within the data engineering team.
Lead architecture reviews, design discussions, and cross-team engineering initiatives.
Work closely with analysts, data scientists, software engineers, and product owners to define and deliver data solutions.
Communicate architectural decisions and trade-offs to technical and non-technical stakeholders.

Required Senior DevOps Engineer, Data Platform
The opportunity
Technical Leadership & Architecture: Drive data infrastructure strategy and establish standardized patterns for AI/ML workloads, with direct influence on architectural decisions across data and engineering teams
DataOps Excellence: Create seamless developer experience through self-service capabilities while significantly improving data engineer productivity and pipeline reliability metrics
Cross-Functional Innovation: Lead collaboration between DevOps, Data Engineering, and ML Operations teams to unify our approach to infrastructure as code and orchestration platforms
Technology Breadth & Growth: Work across the full DataOps spectrum from pipeline orchestration to AI/ML infrastructure, with clear advancement opportunities as a senior infrastructure engineer
Strategic Business Impact: Build scalable analytics capabilities that provide direct line of sight between your infrastructure work and business outcomes through reliable, cutting-edge data solutions
What you'll be doing
Design Data-Native Cloud Solutions - Design and implement scalable data infrastructure across multiple environments using Kubernetes, orchestration platforms, and IaC to power our AI, ML, and analytics ecosystem
Define DataOps Technical Strategy - Shape the technical vision and roadmap for our data infrastructure capabilities, aligning DevOps, Data Engineering, and ML teams around common patterns and practices
Accelerate Data Engineer Experience - Spearhead improvements to data pipeline deployment, monitoring tools, and self-service capabilities that empower data teams to deliver insights faster with higher reliability
Engineer Robust Data Platforms - Build and optimize infrastructure that supports diverse data workloads from real-time streaming to batch processing, ensuring performance and cost-effectiveness for critical analytics systems
Drive DataOps Excellence - Collaborate with engineering leaders across data teams, champion modern infrastructure practices, and mentor team members to elevate how we build, deploy, and operate data systems at scale.

We are looking for an experienced and visionary Data Platform Engineer to help design, build and scale our BI platform from the ground up.

In this role, you will be responsible for building the foundations of our data analytics platform enabling scalable data pipelines and robust data modeling to support real-time and batch analytics, ML models and business insights that serve both business intelligence and product needs.

You will be part of the R&D team, collaborating closely with engineers, analysts, and product managers to deliver a modern data architecture that supports internal dashboards and future-facing operational analytics.

If you enjoy architecting from scratch, turning raw data into powerful insights, and owning the full data lifecycle this role is for you!

Responsibilities
Take full ownership of the design and implementation of a scalable and efficient BI data infrastructure, ensuring high performance, reliability and security.

Lead the design and architecture of the data platform from integration to transformation, modeling, storage, and access.

Build and maintain ETL/ELT pipelines, batch and real-time, to support analytics, reporting, and product integrations.

Establish and enforce best practices for data quality, lineage, observability, and governance to ensure accuracy and consistency.

Integrate modern tools and frameworks such as Airflow, dbt, Databricks, Power BI, and streaming platforms.

Collaborate cross-functionally with product, engineering, and analytics teams to translate business needs into data infrastructure.

Promote a data-driven culture be an advocate for data-driven decision-making across the company by empowering stakeholders with reliable and self-service data access.

We are looking for a Senior Data Engineer to join our Platform group in the Data Infrastructure team.
Youll work hands-on to design and deliver data pipelines, distributed storage, and streaming services that keep our data platform performant and reliable. As a senior individual contributor you will lead complex projects within the team, raise the bar on engineering best-practices, and mentor mid-level engineers while collaborating closely with product, DevOps and analytics stakeholders.
About the Platform group
The Platform Group accelerates our productivity by providing developers with tools, frameworks, and infrastructure services. We design, build, and maintain critical production systems, ensuring our platform can scale reliably. We also introduce new engineering capabilities to enhance our development process. As part of this group, youll help shape the technical foundation that supports our entire engineering team.
Code & ship production-grade services, pipelines and data models that meet performance, reliability and security goals
Lead design and delivery of team-level projects from RFC through rollout and operational hand-off
Improve system observability, testing and incident response processes for the data stack
Partner with Staff Engineers and Tech Leads on architecture reviews and platform-wide standards
Mentor junior and mid-level engineers, fostering a culture of quality, ownership and continuous improvement
Stay current with evolving data-engineering tools and bring pragmatic innovations into the team.

We are looking for an experienced BI Data Engineer to join our Data team within the Information Systems group.
In this role, you will be responsible for building and maintaining scalable, high-quality data pipelines, models, and infrastructure that support business operations across the entire company, with a primary focus on GTM domains.
You will take ownership of core data architecture components, ensuring data consistency, reliability, and accessibility across all analytical and operational use cases.
Your work will include designing data models, orchestrating transformations, developing internal data applications, and ensuring that business processes are accurately represented in the data.
This role requires a combination of deep technical expertise and strong understanding of business operations.
You will collaborate closely with analysts, domain experts, and engineering teams to translate complex business processes into robust, scalable data solutions. If you are passionate about data architecture, building end-to-end data systems, and solving complex engineering challenges that directly impact the business wed love to meet you!
Key Responsibilities:
Design, develop, and maintain end-to-end data pipelines, ensuring scalability, reliability, and performance.
Build, optimize, and evolve core data models and semantic layers that serve as the organizations single source of truth.
Implement robust ETL/ELT workflows using Snowflake, dbt, Rivery, and Python.
Develop internal data applications and automation tools to support advanced analytics and operational needs.
Ensure high data quality through monitoring, validation frameworks, and governance best practices.
Improve and standardize data modeling practices, naming conventions, and architectural guidelines.
Continuously evaluate and adopt new technologies, features, and tooling across the data engineering stack.
Collaborate with cross-functional stakeholders to deeply understand business processes and translate them into scalable technical solutions.

We are looking for an experienced Data Engineering Team Leader.
In this role, you will lead and strengthen our Data Team, drive innovation, and ensure the robustness of our data and analytics platforms.
A day in the life and how youll make an impact:
Drive the technical strategy and roadmap for the data engineering function, ensuring alignment with overall business objectives.
Own the design, development, and evolution of scalable, high-performance data pipelines to enable diverse and growing business needs.
Establish and enforce a strong data governance framework, including comprehensive data quality standards, monitoring, and security protocols, taking full accountability for data integrity and reliability.
Lead the continuous enhancement and optimization of the data analytics platform and infrastructure, focusing on performance, scalability, and cost efficiency.
Champion the complete data lifecycle, from robust infrastructure and data ingestion to detailed analysis and automated reporting, to maximize the strategic value of data and drive business growth.

We are seeking an experienced Senior data Engineer to join our dynamic data team. The ideal candidate will be responsible for designing, building, and maintaining scalable data pipelines and infrastructure, ensuring the availability, reliability, and quality of our data. This role requires strong technical expertise, problem-solving skills, and the ability to collaborate across teams to deliver data -driven solutions.Key Responsibilities:
* Design, implement, and maintain robust, scalable, and high-performance data pipelines and ETL processes.
* Develop and optimize data models, schemas, and Storage solutions to support analytics and Machine Learning initiatives.
* Collaborate with software engineers and product managers to understand data requirements and deliver high-quality solutions.
* Ensure data quality, integrity, and governance across multiple sources and systems.
* Monitor and troubleshoot data workflows, resolving performance and reliability issues.
* Evaluate and implement new data technologies and frameworks to improve the data platform.
* Document processes, best practices, and data architecture.
* Mentor junior data engineers and contribute to team knowledge sharing.

We are looking for a Senior Data Platform Engineer to design, build, and scale next-generation data platform, the backbone powering our AI-driven insights.
This role sits at the intersection of data engineering, infrastructure, and MLOps, owning the architecture and reliability of our data ecosystem end-to-end.
Youll work closely with data scientists,r&d teams, analysts to create a robust platform that supports varying use cases, complex ingestion, and AI-powered analytics.
Responsibilities:
Architect and evolve a scalable, cloud-native data platform that supports batch, streaming, analytics, and AI/LLM workloads across R&D.
Help define and implement standards for how data is modeled, stored, governed, and accessed
Design and build data lakes and data warehouses
Develop and maintain complex, reliable, and observable data pipelines
Implement data quality, validation, and monitoring frameworks
Collaborate with ML and data science teams to connect AI/LLM workloads to production data pipelines, enabling RAG, embeddings, and feature engineering flows.
Manage and optimize relational and non-relational datastores (Postgres, Elasticsearch, vector DBs, graph DBs).
Build internal tools and self-service capabilities that enable teams to easily ingest, transform, and consume data.
Contribute to data observability, governance, documentation, and platform visibility
Drive strong engineering practices
Evaluate and integrate emerging technologies that enhance scalability, reliability, and AI integration in the platform.

We are seeking a Senior DevOps Engineer with extensive experience to lead the design, development, deployment, and operation of large-scale software solutions. This role is a critical bridge between Software Engineering and Infrastructure, demanding a deep proficiency in building and operating reliable, scalable systems within a complex Big Data environment.
What you'll be doing all day:
Own Reliability and Scalability: Lead the architecture and implementation of best practices to ensure high availability, optimal performance, and horizontal scalability of our critical systems, operating within a vast Big Data landscape.
Infrastructure as Code (IaC): Develop, maintain, and evolve our infrastructure using advanced IaC tools (e.g., Terraform or Pulumi), ensuring full automation of service deployment and management across our AWS/GCP cloud environment.
Strategic Collaboration: Partner closely with application software engineering teams to design, conduct code reviews, and implement systems that are stable, secure, and performant.
Observability: Implement and manage robust monitoring, logging, and alerting solutions to enable proactive identification and deep Root Cause Analysis (RCA) of issues.
Automation & Efficiency: Identify and eliminate manual tasks ("Toil") by automating repetitive processes to continuously improve operational efficiency and system reliability.
Production Incident Response: Participate in an on-call rotation to quickly investigate, troubleshoot, and mitigate critical production incidents, driving post-mortems to prevent recurrence.
Performance Engineering: Analyze system performance, conduct performance tuning, and execute capacity planning to meet future demands.

We are seeking a Senior Production Engineer with extensive experience to lead the design, development, deployment, and operation of large-scale software solutions. This role is a critical bridge between Software Engineering and Infrastructure, demanding a deep proficiency in building and operating reliable, scalable systems within a complex Big Data environment.
What You'll Be Doing All Day:
Own Reliability and Scalability: Lead the architecture and implementation of best practices to ensure high availability, optimal performance, and horizontal scalability of our critical systems, operating within a vast Big Data landscape.
Infrastructure as Code (IaC): Develop, maintain, and evolve our infrastructure using advanced IaC tools (e.g., Terraform or Pulumi), ensuring full automation of service deployment and management across our AWS/GCP cloud environment.
Strategic Collaboration: Partner closely with application software engineering teams to design, conduct code reviews, and implement systems that are stable, secure, and performant.
Observability: Implement and manage robust monitoring, logging, and alerting solutions to enable proactive identification and deep Root Cause Analysis (RCA) of issues.
Automation & Efficiency: Identify and eliminate manual tasks ("Toil") by automating repetitive processes to continuously improve operational efficiency and system reliability.
Production Incident Response: Participate in an on-call rotation to quickly investigate, troubleshoot, and mitigate critical production incidents, driving post-mortems to prevent recurrence.
Performance Engineering: Analyze system performance, conduct performance tuning, and execute capacity planning to meet future demands.