Advanced Engineer Data

Role Summary: Design, build, and manage scalable data processing pipelines on the Google Cloud Platform. Drive engineering excellence, data quality, and continuous improvement.

Key Responsibilities: Lead end-to-end design, development, and deployment of scalable data pipelines and integration solutions. Manage multiple delivery workstreams. Translate complex business and technical requirements into robust data engineering solutions.

Develop and optimize ETL/ELT processes using modern cloud, data warehousing, and data processing platforms. Collaborate with technical and business teams to translate complex requirements into robust data solutions. Diagnose and resolve complex production issues through root-cause analysis and system optimization.

Drive technology and process improvements by evaluating emerging tools and engineering best practices. Ensure adherence to enterprise architecture, security, compliance, and performance standards. Maintain high-quality technical documentation, user guides, and solution specifications.

Required Qualifications

Experience: Experience in Data modelling, Data engineering, Data integration.

Experience leading engineering initiatives. Proven experience with cloud tools such as GCP Dataflow, GCP Composer (Airflow), GCP cloud storage.

Strong expertise in cloud platforms: GCP Hands-on experience with data warehousing and processing technologies including: Experience with data streaming tools: Kafka, Cloud Data Flow, Spark Streaming.



Strong experience in data quality and observability platforms to automate quality coverage using ice DQ, Monte Carlo, or similar tools. Solid proficiency with high-level programming and query languages.

Experience collaborating with distributed teams across time zone. Big Query, Lakehouse (Iceberg) Spark Python, SQL

AI Skills (Mandatory): Proficiency in leveraging AI tools for daily engineering tasks to enhance productivity, optimize effort, and ensure cost-aware AI assistance. Familiarity with Retrieval Augmented Generation (RAG) architectures, including semantic layers for data retrieval and grounding Large Language Model (LLM) responses. Conceptual understanding of LLM-based applications (e.g., chatbots, Q&A; systems), encompassing prompt engineering, context management, and response generation. Exposure to agentic and multi-agent AI systems, including agent roles, tool utilization, memory management, and workflow orchestration. Practical experience with LLM application frameworks (e.g., Lang Chain, Lang Graph, or Google Gen AI tools) for prototyping and integrating AI-driven solutions.

Nice to Have: Experience with Power BI/Thought Spot, AI in data engineering, Stonebranch, CI/CD, Databricks and Dev Ops practices. Familiarity with enterprise databases such as DB2, SQL Server, Oracle, Postgre SQL.

Google Cloud Data

Engineer certification is preferred.

Experience in Retail domains (Customer, Stores, e Commerce, Marketing, Supply chain, Merchandising)

📌 Advanced Engineer Data (Bengaluru)
🏢 Albertsons Companies India
📍 Bengaluru