Senior Engineer Data

About Albertsons Companies Inc.:

As a leading food and drug retailer in the United States, Albertsons Companies, Inc. operates over 2,200 stores across 35 states and the District of Columbia. Our well-known banners across the United States, including Albertsons, Safeway, Vons, Jewel-Osco and others, serve more than 36 million U.S customers each week.

We build and shape technology solutions that solve customers problems every day, making things easier for them when they shop with us online or in a store. We have made bold, strategic moves to migrate and modernize our core foundational capabilities, positioning ourselves as the first fully cloud-based grocery tech company in the industry.

Our success is built on a one-team approach, driven by the desire to understand and enhance the customer experience. By constantly pushing the boundaries of retail, we are transforming shopping into an experience that is easy, efficient, fun and engaging.

About Albertsons Companies India:

At Albertsons Companies India, were not just pushing the boundaries of technology and retail innovation, were cultivating a space where ideas flourish and careers thrive. Our workplace in India is a vital extension of the Albertsons Companies Inc. workforce and important to the next phase in the companys technology journey to support millions of customers lives every day.

At the Albertsons Companies India, we are raising the bar to grow across Technology & Engineering, AI, Digital and other company functions, and transform a 165-year-old American retailer. At Albertsons Companies India associates collaborate directly with international teams, enhancing decision-making processes and organizational agility through exciting and pivotal projects. Your work will make history and help millions of lives each day come together around the joys of food and inspire their well-being.

Senior 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 iceDQ, Monte Carlo, or similar tools.
• Strong 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., LangChain, Lang Graph, or Google GenAI tools) for prototyping and integrating AI-driven solutions.

Nice to Have:

• Experience with Power BI/ThoughtSpot, AI in data engineering, Stonebranch, CI/CD, Databricks and DevOpspractices.
• Familiarity with enterprise databases such as DB2, SQL Server, Oracle, PostgreSQL.
• Google Cloud Data Engineer certification is preferred.
• Experience in Retail domains (Customer, Stores, eCommerce, Marketing, Supply chain, Merchandising)