Data Warehouse Architect

**We Want You**:
**You Bring**:

- At least 8 years in a technical role with experience in data architecture, data warehousing, and data engineering.
- At least 3-5 years’ experience working with Apache Kafka and real-time data streaming.
- Strong experience with data warehousing solutions (e.g., AWS RedHat, Snowflake, Google BigQuery).
- Expertise in SQL performance tuning, database optimisation, and complex query development.
- Experience with big data technologies such as Hadoop, Spark, Hive, and Presto.
- Deep understanding of distributed data processing frameworks and parallel computing techniques.
- Strong experience with ETL/ELT processes, data ingestion frameworks, and transformation logic.
- Strong understanding of data governance, security, and compliance best practices.
- Proficiency in programming languages such as Python, Java, or Scala for data processing.
- Experience in designing and implementing high-throughput, low-latency data architectures.
- Strong problem-solving and analytical skills with attention to detail. Experience working with a high volume and complex data environment.
- **W**hat You’ll Do**:
Strategy, Objectives and Execution
- Design and implement scalable, high-performance data warehouse architectures that support analytical and operational workloads.
- Develop and implement long-term technical roadmaps for data management, integration, and processing.
- Lead evaluations and recommend best-fit technologies for real-time and batch data processing.
- Ensure that data solutions are optimised for performance, security, and scalability.
- Identify and resolve bottlenecks in data design and system performance.
- Develop and maintain data models, schemas, and architecture blueprints for relational and big data environments.
- Ensure seamless data integration from multiple sources, leveraging Kafka for real-time streaming and event-driven architecture.
- Facilitate system design and review, ensuring compatibility with existing and future systems.
- Optimise data workflows, ETL/ELT pipelines, and distributed storage strategies.

Technical Expertise
- Architect, build, and maintain Kafka-based streaming platforms for real-time data ingestion, processing, and analytics.
- Design and implementation data lake and data warehouses.
- Develop and enforce data governance policies, ensuring high-quality and secure data management.
- Ensure compliance with data security, privacy, and regulatory standards.
- Utilise advanced SQL query optimisation techniques, indexing strategies, partitioning, and materialised views to enhance performance.
- Work extensively with relational databases (PostgreSQL, MySQL, SQL Server) and big data technologies (Hadoop, Spark).
- Design and implement data architectures that efficiently handle structured and unstructured data at scale.
- Optimise data pipelines and ETL/ELT processes using tools such as Apache, and Spark Streaming.
- Develop automated workflows for data extraction, transformation, and loading (ETL/ELT) across disparate data sources.
- Leverage distributed computing technologies to process and analyse large datasets efficiently.
- Implement best practices for microservices-based data architecture and containerised solutions.

Stakeholder Management
- Collaborate with key internal and external stakeholders to manage expectations and resolve technical issues.
- Ensure operational communication is effectively documented and shared across relevant Teams.
- Present architectural strategies, progress, and recommendations to senior leadership and stakeholders.
- Translate complex technical concepts into understandable terms for non-technical stakeholders.

Continuous Improvement and Innovation
- Drive adoption of new technologies and methodologies in data warehousing, big data, and streaming analytics.
- Challenge the status quo to find new efficiencies and innovations in large-scale data processing.
- Conduct performance tuning, capacity planning, and scalability assessments for data systems handling petabyte-scale datasets.
- Research and implement emerging technologies in distributed computing, data warehouses, and real-time analytics.**

**Living the Spirit**:

- Foster an inclusive and collaborative engineering culture.
- Encourage open communication and knowledge-sharing within the team.
- Promote continuous learning and innovation within the data architecture space.
- Lead by example and demonstrate integrity in all professional interactions.

**The Company We Keep**:
At BET Software, we don't just recruit talent, we cultivate it. Our learning and development programmes, our various opportunities for growth, and our well-deserved incentives are what keep our All-Star Team the best amongst the rest.

**Please note that only Team members who meet the stipulated minimum requirements will be considered.