Data Engineer

About the Job\n\nSkills\n\n

data lakehouse

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python

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etl pipelines

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Data Engineering

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apache hop

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Databases

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NoSQL

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SQL

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Location: Bangalore (5 days work from office, HSR Layout)

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Job Type: Full Time

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Industry: Agritech

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Openings: Multiple- junior, mid level etc

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Python- Mandatory

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Overview

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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.

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Key Responsibilities

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%CF; 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.

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

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

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

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%CF; 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.

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

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

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

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

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Required Qualifications

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

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

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%CF; Technical Skills:

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%CB; Proficiency in programming languages such as Python, Java, or Scala.

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

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

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

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

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

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%CF; Soft Skills:

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%CB; Excellent problem-solving and analytical skills.

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%CB; Strong communication and collaboration abilities.

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

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

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Preferred Qualifications

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%CF; Experience with real-time data processing and streaming architectures.

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%CF; Familiarity with machine learning pipelines and MLOps practices.

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

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

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Work Environment

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

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%CF; Hours: Full-time, with occasional on-call responsibilities for critical data systems