Senior Data Engineer

Company: KJBN Labs

Location: Bangalore (Hybrid)

Overview

The Senior Data Engineer will design, develop, and maintain scalable data pipelines and infrastructure to support data-driven decision-making and advanced analytics. This role requires deep expertise in data engineering, strong problem-solving skills, and the ability to collaborate with cross-functional teams to deliver robust data solutions.

Key Responsibilities

● Data Pipeline Development: Design, build, and optimize scalable, secure, and reliable data pipelines to ingest, process, and transform large volumes of structured and unstructured data.

● Data Architecture: Architect and maintain data storage solutions, including data lakes, data warehouses, and databases, ensuring performance, scalability, and cost-efficiency.

● Data Integration: Integrate data from diverse sources, including APIs, third-party systems, and streaming platforms, ensuring data quality and consistency.

● Performance Optimization: Monitor and optimize data systems for performance, scalability, and cost, implementing best practices for partitioning, indexing, and caching.

● Collaboration: Work closely with data scientists, analysts, and software engineers to understand data needs and deliver solutions that enable advanced analytics, machine learning, and reporting.

● Data Governance: Implement data governance policies, ensuring compliance with data security, privacy regulations (e.g., GDPR, CCPA), and internal standards.

● Automation: Develop automated processes for data ingestion, transformation, and validation to improve efficiency and reduce manual intervention.

● Mentorship: Guide and mentor junior data engineers, fostering a culture of technical excellence and continuous learning.

● Troubleshooting: Diagnose and resolve complex data-related issues, ensuring high availability and reliability of data systems.

Required Qualifications

● Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.

● Experience: 5+ years of experience in data engineering or a related role, with a proven track record of building scalable data pipelines and infrastructure.

● Technical Skills:

○ Proficiency in programming languages such as Python, Java, or Scala.

○ Expertise in SQL and experience with NoSQL databases (e.g., MongoDB, Cassandra).

○ Strong experience with cloud platforms (e.g., AWS, Azure, GCP) and their data services (e.g., Redshift, BigQuery, Snowflake).

○ Hands-on experience with ETL/ELT tools (e.g., Apache Airflow, Talend, Informatica) and data integration frameworks.

○ Familiarity with big data technologies (e.g., Hadoop, Spark, Kafka) and distributed systems.

○ Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes) is a plus.

● Soft Skills:

○ Excellent problem-solving and analytical skills.

○ Strong communication and collaboration abilities.

○ Ability to work in a fast-paced, dynamic environment and manage multiple priorities.

● Certifications (optional but preferred): Cloud certifications (e.g., AWS Certified Data Analytics, Google Professional Data Engineer) or relevant data engineering certifications.

Preferred Qualifications

● Experience with real-time data processing and streaming architectures.

● Familiarity with machine learning pipelines and MLOps practices.

● Knowledge of data visualization tools (e.g., Tableau, Power BI) and their integration with data pipelines.

● Experience in industries with high data complexity, such as finance, healthcare, or e-commerce.

Work Environment

● Team: Collaborative, cross-functional team environment with data scientists, analysts, and business stakeholders.

● Hours: Full-time, with occasional on-call responsibilities for critical data systems.