Posted:1 day ago|
Platform:
On-site
Full Time
Senior Data Engineer (On-Site, India) About The Opportunity A high-growth innovator in the Analytics & Enterprise Data Management sector, we architect and deliver cloud-native data platforms that power real-time reporting, AI/ML workloads, and intelligent decisioning for global retail, fintech, and manufacturing leaders. Our engineering teams transform raw, high-velocity data into trusted, analytics-ready assets that drive revenue acceleration and operational excellence. Role & Responsibilities Design, build, and optimise batch and streaming ETL/ELT pipelines on Apache Spark and Kafka, ensuring sub-minute latency and 99.9% uptime. Develop modular, test-driven Python code to ingest, cleanse, and enrich terabyte-scale datasets from relational, NoSQL, and API sources. Model data for analytics and AI, implementing star/snowflake schemas, partitioning, and clustering in BigQuery, Redshift, or Snowflake. Automate workflow orchestration with Apache Airflow, defining DAGs, dependency management, and robust alerting for SLA adherence. Collaborate with Data Scientists and BI teams to expose feature stores, curated marts, and self-service semantic layers. Enforce data-governance best practices—lineage, cataloguing, RBAC, and encryption—in compliance with GDPR and SOC 2 standards. Skills & Qualifications Must-Have 3–6 years hands-on engineering large-scale data pipelines in production. Expertise in Python and advanced SQL for ETL, optimisation, and performance tuning. Proven experience with Spark (PySpark or Scala) and streaming technologies such as Kafka or Kinesis. Deep knowledge of relational modelling, data-warehousing concepts, and at least one cloud DWH (BigQuery, Redshift, or Snowflake). Solid command of CI/CD, Git workflows, and containerisation (Docker). Preferred Exposure to infrastructure-as-code (Terraform, CloudFormation) and Kubernetes. Experience integrating ML feature stores and monitoring data quality with Great Expectations or similar tools. Certification on AWS, GCP, or Azure data services. Benefits & Culture Highlights On-site, engineer-first environment with dedicated lab space and latest Mac/Linux gear. Rapid career progression through technical mentorship, sponsored certifications, and conference budgets. Inclusive, innovation-driven culture that rewards outcome ownership and creative problem-solving. Ready to architect next-gen data pipelines that power AI at scale? Apply now and join a mission-focused team turning data into competitive advantage. Skills: airflow,docker,snowflake,apache spark,data engineering,redshift,sql,pyspark,python,data modeling,bigquery,ci/cd,apache airflow,data warehousing,spark,etl,kafka,git
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