About the role
We are seeking a highly skilled Data Engineer to join our Data & Analytics team. You will be responsible for designing, building, and optimizing customer Unified Data Platform (UDP)—a modern, cloud-native ecosystem supporting enterprise-wide ingestion, transformation, and AI-ready analytics.
You will act as a foundational architect, establishing engineering standards and governance frameworks. By partnering with diverse business units, you will ensure our data is accurate, secure, and strategically positioned to drive measurable business value through BI and AI/ML. Whether the underlying infrastructure is native cloud (AWS/Azure/GCP) or specialized platforms like Databricks or Snowflake, you will ensure the architecture is robust and scalable
Requirements:
- Bachelor's/Master's in Computer Science, Information Systems, or a related field.
- Relevant Cloud or Platform Certifications (e.g., Databricks Certified Data Engineer,
- Snowflake SnowPro Core, or AWS/Azure/GCP Data Engineer) are highly valued.
- Experience: 3–7 years in Data Engineering, specifically building and scaling cloudnative data platforms.
- Cloud Proficiency: Strong expertise in at least one major cloud data stack (AWS, Azure, or GCP) and the ability to work in multi-cloud environments.
- Core Languages: Mastery of SQL and Python/PySpark.
- Integration: Proven experience integrating enterprise ERP data sources.
- API & Connectivity: Strong knowledge of API development and integration (REST, GraphQL, Webhooks).
- Data Modeling: Solid understanding of 3NF, Star Schema, Dimensional modeling, and Data Vault basics.
- Engineering Mindset: Ability to design platform-wide standards and lightweight governance (Data Quality checks, Cataloging).
Optional Capabilities:
- Platform Expertise: Proficiency in Databricks (Unity Catalog, Delta Live Tables) or Snowflake (Snowpark, Data Sharing, Performance Tuning).
- Ecosystem Tools: Experience with dbt, BigQuery, or similar modern data stack components.
- Real-time Processing: Familiarity with streaming ingestion (e.g., Kafka, Kinesis, or Pub/Sub).
Responsibilities:
- Data Architecture & Standards: Define and implement Medallion Architecture (Bronze, Silver, Gold zones), storage patterns, and schema evolution standards.
- Establish reusable naming conventions and partitioning strategies.
- Pipeline Engineering: Design, build, and maintain scalable ETL/ELT pipelines using modern orchestration tools (e.g., Airflow, Step Functions, or dbt).
- Multi-Source Ingestion: Build robust connectors to ingest data from SAP/ERP systems, custom applications, REST/GraphQL APIs, flat files, and IoT streams.
- Performance & Cost Optimization: Audit and optimize complex queries and transformation logic to balance processing speed with cloud cost-efficiency.
- Data Governance & Quality: Implement automated data validation, metadata cataloging, data lineage, and granular access controls (IAM/RBAC).
- Business Partnership: Act as a technical consultant for business units, translating functional requirements into high-performance data deliverables.
- Downstream Enablement: Provide structured, high-quality datasets for Power BI/Tableau dashboards and feature stores for AI/ML pipelines.
Due to high volume of job applications, only shortlisted candidates will be contacted for interview.
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