Data Engineer

About the Job Skills data lakehouse python etl pipelines Data Engineering apache hop Databases NoSQL SQL Location: Bangalore (5 days work from office, HSR Layout) Job Type: Full Time Industry: Agritech Openings: Multiple- junior, mid level etc Python- Mandatory   Overview The Data Engineer/ 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