About the role

As a Senior Data Engineer II (P50), you are an experienced data engineer who consistently delivers high‑impact outcomes across both short and long time horizons, leading domains that span multiple services and business areas. You combine deep data engineering expertise with strong business acumen to guide the roadmap and architecture for your area, elevate the team’s overall capabilities, and act as a senior escalation point for technical and operational issues.

You spearhead the end‑to‑end data solutions—from ingestion and modeling through quality, observability, and consumption—while mentoring P20/P30 engineers and driving process and automation improvements across the team.

This hybrid position requires 3 days per week in our Bangalore office and 2 days working remotely. This position requires working hours in IST. Relocation assistance will not be provided for this role.

What you’ll do

• Lead end‑to‑end data solutions across multiple subject areas and services, from requirements and design through implementation, rollout, and ongoing ownership.
• Architect, build, and maintain large‑scale, production‑grade data pipelines (batch and/or streaming) using modern data lake and warehouse technologies (e.g., Databricks, Delta, cloud data platforms) and industry‑standard ETL/ELT patterns.
• Design robust data models and curated “golden” datasets that power analytics, dashboards, and data science use cases, including facts, dimensions, snapshots, and SCDs.
• Own data quality, reliability, and SLAs for your domains: define contracts, implement validation and monitoring, and drive root‑cause analysis and long‑term fixes for data incidents.
• Influence team and cross‑functional roadmaps and OKRs by translating business goals into multi‑quarter technical plans, tradeoffs, and investment proposals.
• Partner closely with cross‑functional teams (e.g., Finance, Sales Ops, RevOps, Product, Data Science & Analytics) to understand requirements and deliver scalable data solutions that unlock insights and automation.
• Lead incident response and complex technical investigations for your areas, coordinating across teams, driving clear communication, and ensuring durable remediation.
• Mentor and uplevel other engineers, providing code and design reviews, pairing, and structured feedback to grow the team’s technical mastery and operational excellence.
• Improve processes, documentation, and tooling for data development, testing, deployment, and observability; standardize best practices across BizTech Data & Integrations.
• Champion Samsara’s cultural principles—Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, and Win as a Team—in how you design systems and collaborate across the company.

Minimum qualifications

• 10+ years of relevant experience in data engineering or closely related roles, with demonstrated impact and career progression.
• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Electrical Engineering, Computer Engineering, or related discipline.
• Expert-level data engineering skills, including ETL/ELT, data modeling, and working with large-scale, cloud-based data lake/warehouse stacks and modern tooling (e.g., Spark, SQL engines, orchestration frameworks).
• Strong programming skills in SQL and at least one general-purpose language (e.g., Python), with experience building and maintaining production-grade pipelines and reusable frameworks.
• Proven experience designing and operating critical data models and curated datasets for analytics, reporting, and/or ML use cases, including performance tuning and cost optimization.
• Demonstrated experience in AI/ML systems, including building, deploying, or supporting machine learning pipelines, feature engineering workflows, and integrating predictive or generative models into production data systems.
• Hands-on experience with AI application and agent development, including designing and deploying intelligent agents, workflow automation, or LLM-powered applications (e.g., retrieval-augmented generation, tool-using agents, conversational systems), with a focus on scalability, observability, and reliability.
• Demonstrated ability to lead multi-stakeholder projects over 2+ quarters, managing tradeoffs between technical quality, speed, and business impact.
• Solid knowledge of Databricks features and administration, including Unity Catalog, cluster management, security, troubleshooting, root-cause analysis, and performance optimization.
• Strong communication and collaboration skills, including clear written design docs and effective communication with senior stakeholders and cross-functional partners.
• Track record of mentoring and acting as an escalation point for more junior engineers or analysts.

Experience with the following:

• 5+ years in Python, SQL.
• Exposure to ETL tools such as Fivetran, DBT or equivalent.
• API: Experience with Python-based API frameworks for building and serving data/AI pipelines.
• RDBMS: MySQL, AWS RDS/Aurora MySQL, PostgreSQL, Oracle, MS SQL Server, or equivalent.
• Cloud: AWS, Azure, and/or GCP.
• Data warehouse: Databricks, Google BigQuery, AWS Redshift, Snowflake or equivalent.
• Experience integrating AI/ML platforms and tooling (e.g., MLflow, feature stores, vector databases, model serving infrastructure) into data ecosystems.
• Familiarity with LLM ecosystems, including prompt engineering, embeddings, vector search, and evaluation frameworks for AI applications.
• Hands-on experience with modern AI developer tools and assistants, such as Claude, Cursor, GitHub Copilot, and ChatGPT, for accelerating development, code generation, debugging, and workflow automation.
• Experience in SaaS, B2B, or data-heavy product environments, especially where IoT, telemetry, or event-driven data plays a central role.
• Hands-on experience with Databricks or similar Spark-based platforms, Delta/Parquet data lakes, and modern data orchestration tools.
• Background working with finance, sales, or revenue operations data, and comfort modeling business processes such as bookings, billings, and pipeline.
• Prior experience driving cross-team data initiatives (e.g., standardizing metrics, centralizing business logic, or implementing shared data governance frameworks)

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