▷ (Apply Now) Data Engineer

1 week ago

Bengaluru India Wisemonk Full time

Job Description One of our US Based client who is transforming how people buy and sell secondhand apparel, and data is central to that mission. We're looking for a Data Engineer to join our Data Engineering team with a focus on analytics engineering and enabling scalable, self-service data across the organization. In this role, you'll design, build, and maintain scalable data infrastructure and robust pipelines to support a wide range of analytics use cases. You'll collaborate with cross-functional partners to develop trusted data models that power reporting, analytics, and automation. You'll also contribute to platform-level efforts around content governance and performance optimization. This is a foundational role for someone who is eager to deepen their cloud-native data engineering skills, explore the application of AI in modern data workflows and drive business impact. What you will do Design, implement, and maintain scalable data and analytics infrastructure to support diverse datasets, ensuring high availability, performance, and data integrity. Build and optimize robust ELT pipelines using dbt and Databricks to ingest and transform structured and semi-structured data across business domains. Develop accessible and trusted data models to enable self-service analytics and operational reporting. Collaborate with analysts, data scientists, and product managers to understand stakeholder needs and deliver actionable insights. Contribute to platform usability and governance through workspace organization, access management, and performance tuning. Participate in debugging, monitoring, and ensuring data quality across production pipelines. Stay curious about emerging technologies, with an emphasis on incorporating AI capabilities and automation into workflows. Qualification 4+ yrs in data/analytics engineering, strong SQL + Python/Spark, hands-on with Databricks & dbt Strong development expertise in Python with experience building and deploying robust services, adhering to software engineering principles. Proficiency in SQL and data modeling for analytics use cases, with a strong understanding of data quality and semantic principles. Experience building distributed data pipelines using Spark, Databricks, or cloud-native data tools. Familiarity with AI/ML workflows and an eagerness to explore their application in production environments, particularly regarding feature engineering and AI experimentation. Proficiency in building data pipelines using Airflow and Apache Spark (RDD/DataFrames/Structured Streaming) Experience with distributed messaging systems like Kafka, SQS, or RabbitMQ. Knowledge of big data lake architectures using S3, Parquet/Avro, and Delta Lake in Databricks. Strong hands-on experience with schema validation (e.g., Pydantic, Avro) and data QA practices Comfort with Docker, Kubernetes/ECS, and production deployment workflows. Working knowledge of CI/CD, infrastructure-as-code, and monitoring tools like Datadog. Familiarity with building REST/gRPC APIs and data-serving interfaces. Ability to write scalable, reusable, and testable code with a focus on performance, cost, and reliability. Understanding of CI/CD tooling, structured testing frameworks, and system-level monitoring. Excellent problem-solving and collaboration skills across engineering, product, and analytics teams. What you'll own : ELT pipelines, Analytics infra, data models for self-service analytics, platform governance - all with an AI first mindset

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