Senior Data Engineer

We are looking for a Senior Data Engineer with a deep understanding of Apache Spark (Scala & PySpark), Kafka Streams (Java), AWS services, Snowflake, Apache Iceberg, Tableau, and Data Lake architectures. As a senior member of our team, you will be responsible for leading the design, implementation, and optimization of large-scale data systems, real-time streaming solutions, and cloud-based data platforms. You will work with other engineers to deliver high quality data solutions, mentor junior team members, and collaborate closely with cross-functional teams to solve complex business problems.

Key Responsibilities:

• Lead the design and development of scalable, high-performance data architectures on AWS, leveraging services such as S3, EMR, Glue, Redshift, Lambda, and Kinesis. Architect and manage Data Lakes for handling structured, semi-structured, and unstructured data.
• Design and build complex data pipelines using Apache Spark (Scala & PySpark), Kafka Streams (Java), and cloud-native technologies for batch and real-time data processing. Optimize these pipelines for high performance, scalability, and cost-effectiveness.
• Develop and optimize real-time data streaming applications using Kafka Streams in Java. Build reliable, low-latency streaming solutions to handle high-throughput data, ensuring smooth data flow from sources to sinks in real-time.
• Manage Snowflake for cloud data warehousing, ensuring seamless data integration, optimization of queries, and advanced analytics. Implement Apache Iceberg in Data Lakes for managing large-scale datasets with ACID compliance, schema evolution, and versioning.
• Design and maintain highly scalable Data Lakes on AWS using S3, Glue, and Apache Iceberg. Ensure data is easily accessible, stored in optimal formats, and well-integrated with downstream analytics systems.
• Work with business stakeholders to create actionable insights using Tableau. Build data models and dashboards that drive key business decisions, ensuring that data is easily accessible and interpretable.
• Continuously monitor and optimize Spark jobs, Kafka Streams processing, and other cloud-based data systems for performance, scalability, and cost. Implement best practices for stream processing, batch processing, and cloud resource management.
• Lead and mentor junior engineers, fostering a culture of collaboration, continuous learning, and technical excellence. Ensure high-quality code delivery, adherence to best practices, and optimal use of resources.
• Work closely with Data Scientists, Product Managers, and DevOps teams to understand business needs and deliver impactful data solutions. Participate in technical discussions, from system design to data governance.
• Ensure that data pipelines, architectures, and systems are thoroughly documented and follow coding and design best practices. Promote knowledge-sharing across the team to maintain high standards for quality and scalability.

Preferred candidate profile

• Bachelors or Masters degree in Computer Science, Engineering, or related field (or equivalent work experience).
• 5+ years of experience in Data Engineering or a related field, with a proven track record of designing, implementing, and maintaining large-scale distributed data systems.
• Proficiency in Apache Spark (Scala & PySpark) for distributed data processing and real-time analytics.
• Hands-on experience with Kafka Streams using Java for real-time data streaming applications.
• Strong experience in Data Lake architectures on AWS, using services like S3, Glue, EMR, and data management platforms like Apache Iceberg.
• Proficiency in Snowflake for cloud-based data warehousing, data modeling, and query optimization.
• Expertise in SQL for querying relational and NoSQL databases, and experience with database design and optimization.
• Strong Experience in building ETL pipelines using Spark(Scala & Pyspark) and maintain them.
• Proficiency in Java, particularly in the context of building and optimizing Kafka Streams applications for real-time data processing.
• Experience with AWS services (e.g., Lambda, Redshift, Athena, Glue, S3) and managing cloud infrastructure.
• Expertise with Apache Iceberg for handling large-scale, transactional data in Data Lakes, supporting versioning, schema evolution, and partitioning.
• Experience with Tableau for business intelligence, dashboard creation, and data visualization is a plus.
• Knowledge of CI/CD tools and practices, particularly in data engineering environments.
• Familiarity with containerization tools like Docker and Kubernetes for managing cloud-based services.

Soft Skills:

• Excellent problem-solving skills, with a strong ability to debug and optimize large-scale distributed systems.
• Strong communication skills to engage with both technical and non-technical stakeholders.
• Proven leadership ability, including mentoring and guiding junior engineers.
• A collaborative mindset and the ability to work across teams to deliver integrated solutions.