Head of Data Engineering

BTSE Group is a global leader in fintech and blockchain technology, anchored by three core business pillars: Exchange, Payments, and Infrastructure Development. Serving over 100 corporate clients worldwide, we provide white-label exchange and payment solutions. Our offerings encompass everything from exchange infrastructure hosting and development to custody, wallets, payments, blockchain integration, trading, and more. We are looking for talented professionals in marketing, operations, customer support, and other departments. The roles offered may be on-site, remote, or hybrid, in collaboration with our local partner.

About the Opportunity:

We are looking for a Head of Data Engineering to lead the design and implementation of our data infrastructure pipeline, supporting our rapidly expanding crypto exchange operations. This role will focus on managing and architecting systems capable of handling massive volumes of real-time transaction data, user behavior data, market feeds, and other critical information for various business functions. You will work closely with cross-functional teams to ensure the availability, scalability, and cost-efficiency of the data systems.

The ideal candidate is a hands-on leader who can build and scale complex data infrastructures, optimize data storage and processing, and ensure that our systems are fast, secure, and compliant.

Responsibilities:

• Data Architecture and Infrastructure Design: Lead the design and architecture of scalable, high-performance data systems capable of storing and processing vast amounts of transactional data, user behavior logs, market data, and trading activity. Ensure systems are highly available and optimized for low-latency access.
• Real-Time Data Processing: Architect and implement systems that handle high-throughput, real-time data streams from cryptocurrency transactions, market feeds, and user activities on the platform. Ensure data is ingested and processed with minimal delay to allow for timely decision-making and trading execution.
• Data Integration and Management: Design and implement robust systems for ingesting, storing, and integrating data from various sources, including transaction data, user logs, trading activity, and external data feeds such as market data or news sources. Normalize and organize this data to support analytics and business intelligence needs.
• User and Transaction Data Storage: Develop strategies for storing large volumes of user behavior data (e.g., transaction history, trading patterns, login activity) and transaction data (e.g., order books, trades, withdrawals). Ensure that data is accessible in a fast, secure, and cost-effective manner.
• Data Governance, Quality, and Compliance: Establish strong data governance practices to ensure data integrity, quality, and compliance with regulations (e.g., GDPR, CCPA, AML/KYC requirements). Implement data validation, cleansing, and auditing processes to maintain high-quality and reliable datasets.
• Cost Optimization: Design systems that balance performance with cost-efficiency, using techniques such as sharding, data partitioning, and compression. Ensure that the data infrastructure scales without unnecessary expense as the business grows.
• Leadership and Team Management: Lead and mentor a team of data engineers, driving best practices, performance standards, and fostering a collaborative culture. Provide guidance and career development to help team members grow professionally.
• Cross-Department Collaboration: Work closely with product, marketing, research, security, and compliance teams to ensure that data infrastructure meets the diverse needs of the business. Facilitate the development of analytical frameworks that support product innovation, user behavior analysis, and regulatory compliance.
• Technology Evaluation and Selection: Continuously evaluate emerging data technologies, ensuring that the organization uses the most effective tools for managing transaction and user data. Ensure that the data infrastructure is aligned with the company's strategic goals and technical requirements.

Requirements:

• Technical Leadership: Proven experience leading and managing data engineering teams, with a focus on building and scaling data infrastructures that support high-frequency, real-time transactions, user behavior analysis, and large-scale data storage.
• Data Architecture Expertise: Strong background in designing systems that handle large-scale transactional data, real-time streaming data, and high-volume user behavior data. Expertise in data storage systems like distributed databases and data lakes is essential.
• Real-Time Data Processing: Hands-on experience with real-time data processing frameworks (e.g., Apache Kafka, Apache Flink, Apache Spark Streaming) and the ability to design low-latency systems to process crypto transaction data in real-time.
• Data Integration and Management: Experience integrating diverse data sources (e.g., transaction data, market data, user logs) into a unified platform while ensuring consistency, availability, and performance across systems.
• Cost Optimization: Ability to balance performance with cost-efficiency when designing data storage and processing systems for high-volume data, including implementing data partitioning, sharding, and compression techniques.
• Security and Compliance Expertise: Strong understanding of data security practices, including encryption, access control, and compliance with financial regulations (e.g., AML/KYC). Familiarity with cryptocurrency-specific regulations is a plus.
• Data Governance & Quality Control: Proven ability to implement data governance processes that ensure the integrity and quality of large datasets. Experience with data lineage, validation, and auditing processes.
• Data Storage & Management Techniques: Experience with high-performance distributed database management and efficient storage strategies.
• Programming Languages: Proficiency in languages like Python, Java, or Scala for data engineering tasks. Experience with SQL and NoSQL databases (e.g., PostgreSQL, Cassandra, MongoDB).
• Distributed Systems & Performance Optimization: Knowledge of distributed computing systems for big data storage and processing (e.g., Apache Hadoop, Apache Cassandra, HBase, or custom sharded architectures). Experience in optimizing systems for performance under heavy load.
• Scalability & High Availability: Experience designing and maintaining scalable, high-availability data infrastructure for mission-critical applications with a focus on minimizing downtime and latency.
• Containerization and Orchestration: Familiarity with containerization (Docker) and orchestration platforms (Kubernetes) to manage microservices and large-scale data workflows.
• Cross-Functional Collaboration & Stakeholder Management: Ability to collaborate across different teams (e.g., product, compliance, research) to ensure the data infrastructure aligns with the business's strategic needs and regulatory requirements.
• Data Modeling & Metadata Management: Expertise in designing data models optimized for querying and analytics. Experience managing metadata to ensure smooth access to structured data for internal teams.

Nice to Haves:

• A degree in Computer Science, Engineering, Mathematics, or a related field.
• Experience working in a financial, trading, or cryptocurrency environment.
• Familiarity with blockchain technologies and cryptocurrency trading platforms.
• Strong understanding of data privacy and security standards relevant to financial services and crypto exchanges (e.g., PCI DSS, GDPR).
• Experience with distributed ledgers or blockchain technologies.

Perks and Benefits:

• Various team building programs and company events.
• And many more Apply and let us tell you more