Senior Data Engineer

Key Responsibilities

• Design, build, and maintain robust and scalable data pipeline architectures.
• Assemble large, complex datasets that meet both functional and non-functional business requirements.
• Identify, design, and implement internal process improvements — including automation of manual workflows, optimization of data delivery, and re-architecting infrastructure for greater scalability and reliability.
• Design, build, and optimize ETL infrastructure to enable scalable, high-quality data workflows across diverse sources, leveraging SQL and modern data processing frameworks.
• Build analytics tools that utilize the data pipeline to deliver actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
• Collaborate with stakeholders across Executive, Product, Data, and Design teams to resolve data-related technical issues and ensure their data infrastructure needs are met.
• Ensure data integrity, separation, and security across multiple data centers and AWS regions.
• Create data tools and frameworks to empower analytics and data science teams in building and optimizing products that drive innovation and establish market leadership.
• Lead and mentor a small team of data engineers, fostering a culture of technical excellence, collaboration, and continuous improvement.
• Provide technical guidance, set coding standards, conduct code reviews, and support career development for team members.
• Work closely with data and analytics experts to continually enhance the functionality, reliability, and scalability of our data systems.

Data Engineering And Infrastructure

• 6+ years of experience in a Data Engineering role, designing, building, and managing scalable and reliable data systems.
• Proficient with big data and stream-processing technologies such as Spark and Kafka.
• Hands-on experience with cloud platforms, particularly AWS services like EC2 and RDS.
• Skilled in building and orchestrating data pipelines using tools like Airflow.
• Experience with Databricks for scalable data processing and advanced analytics.
• Strong knowledge of SQLMesh for modern data workflow management.
• Extensive experience integrating and working with external data sources via REST APIs, GraphQL endpoints, and SFTP servers.
• Strong communication skills and leadership capabilities are required.

Databases And Data Management

• Expertise with relational and NoSQL databases, including Postgres and MongoDB.
• Solid understanding of data modeling, data governance, and data security best practices.

Programming And Development

• Proficient in Python for data engineering, automation, and workflow scripting.
• Familiarity with software engineering best practices, including version control, testing, and CI/CD pipelines for data workflows.
• Experience with JavaScript and TypeScript is a plus.

Analytics, Visualization, And BI

• Skilled in implementing and supporting self-service BI tools to enable business teams with accessible, actionable insights.
• Experience with Streamlit for building interactive data visualizations is a plus.

Blockchain And Financial Data Expertise

• Knowledge of blockchain technology and the cryptocurrency ecosystem is a nice-to- have, with a strong interest in staying up to date with emerging trends.
• Experience working with financial datasets and financial engineering concepts is considered a strong advantage.

Our Stack We work with a modern and evolving technology stack, including but not limited to:

• Cloud Infrastructure: AWS for cloud services and infrastructure management
• Databases: PostgreSQL for relational data, MongoDB for non-relational (NoSQL) data, and Redis for caching and real-time data management
• Backend: NestJS (Node.js, TypeScript) and Python for building scalable backend services
• Frontend: React for web applications, Streamlit for building interactive data visualizations
• Data Engineering: Airflow and SQLMesh for data pipeline orchestration and modern workflow management
• Big Data & Processing: Databricks and Kafka for scalable data processing, analytics, and streaming
• Integrations & APIs: Extensive use of REST APIs, GraphQL, SFTP, and Slack integrations to enable seamless data exchange and operational workflows

Messaging&EventStreaming: Kafkaforreal-timedatapipelinesandevent-driven architectures