Senior Data Engineer

Job not on LinkedIn

🔥 26 minutes ago

🇮🇳 India – Remote

⏰ Full Time

🟠 Senior

🚰 Data Engineer

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Bidgely

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2011

⚡ Energy

🤖 Artificial Intelligence

☁️ SaaS

💰 $18M Venture Round - Bidgely on 2023-07

Energy • Artificial Intelligence • SaaS

Bidgely is an energy analytics company that provides utilities and energy providers with AI-powered software for customer energy insights, meter data analytics, and appliance-level energy disaggregation (non-intrusive load monitoring). Its platform uses machine learning to analyze smart meter and IoT data to enable customer engagement, demand response, energy efficiency programs, and distributed energy resource (DER) planning. Bidgely sells to enterprise utility customers as a B2B SaaS solution focused on improving customer experience and operational energy programs.

📋 Description

• Build and run central data platform features on Databricks and Snowflake • Design, build, and maintain scalable batch and streaming ELT/ETL pipelines and large-scale datasets • Build AI agents and agentic data products using Databricks, Snowflake, LangGraph, and FastMCP • Implement automated data quality checks, end-to-end lineage tracking, and RBAC security policies • Monitor and optimize Databricks and Snowflake compute and storage costs • Translate business requirements into scalable, well-modelled analytical solutions with product managers, BI engineers, and data scientists • Support Kimball dimensional and 3NF data modelling for AWB reporting and analytics • Contribute to CI/CD pipelines for data workflows, including version control, automated testing, and deployment standards • Review code and share best practices • Translate architectural designs into production-ready, modular, reusable, loosely coupled, and testable code • Deliver core data platform features end to end in production within the first 12 months • Deliver agent-ready datasets and tools for LangGraph agents and MCP tools • Ensure data quality, lineage, and RBAC for every dataset built • Keep pipeline compute and storage costs within agreed budgets • Individual contributor role with no direct reports

🎯 Requirements

• 5+ years of data engineering experience building central data platforms, large-scale pipelines, and production datasets • Strong, hands-on proficiency in both Databricks and Snowflake is strictly required • Strong proficiency in Apache Spark using Python or Scala on Databricks • Strong SQL proficiency on Databricks and Snowflake • Strong coding skills • Hands-on experience with Delta Lake, including MERGE, schema evolution, OPTIMIZE, VACUUM, and performance tuning • Hands-on experience building streaming pipelines with Kafka and Spark Structured Streaming, including duplicates, late data, checkpoints, and delivery guarantees • Production experience orchestrating workflows with Apache Airflow; mandatory • Hands-on experience building AI agents with LangGraph or LangChain and agent features of Databricks and Snowflake, such as Genie and Cortex • Experience using AI code-generation tools in day-to-day engineering work • Understanding of data governance, data lineage, quality monitoring, regulatory compliance, RBAC, and governance frameworks • Experience building production-grade data pipelines on AWS, Azure, or GCP with reliability and observability focus • Understanding of Kimball dimensional modelling, star/snowflake schemas, and 3NF normalization • Familiarity with CI/CD pipelines, Git-based workflows, and DevOps practices for data engineering • Strong communication and stakeholder management skills • Ability to work independently across time zones • Bonus: exposure to Databricks and Snowflake Native Applications, data clean rooms, or Marketplace integrations • Bonus: experience with dbt models, Prefect, dbt Cloud, semantic layer tools such as Looker or Tableau, Agile/product-based development, cost optimization, energy/utilities/IoT analytics, or MLOps

🏖️ Benefits

• Remote work arrangement • No other benefits, perks, or compensation extras are specified

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