Sr. Databricks Data Engineer

Duration: Long-Term / Renewable Engagement

Location: Remote — Latin America (candidates based in Argentina preferred; Córdoba hub available)

Time Zone Requirement: Overlap with US Pacific Time business hours (approx. 11:00 AM–7:00 PM PT); exact schedule to be aligned with the business unit and is open to negotiation

Start Date: As soon as candidate is approved by client

About Marvell

Marvell Technology is a leading semiconductor company specializing in data infrastructure solutions, spanning the data center core to the network edge. Headquartered in Santa Clara, California, Marvell designs and scales complex System-on-a-Chip architectures that power cloud, AI, 5G, enterprise networking, storage, and automotive applications for many of the world’s largest hyperscalers and technology companies.

Beyond engineering silicon, Marvell runs a large, data-intensive internal operation spanning more than 20 business units worldwide — from microchip engineering to program management and human resources — all of which depend on reliable, well-modeled data to make decisions. This role sits inside that internal data organization, supporting business units directly with hands-on, end-to-end analytics engineering.

Our culture is:

• Fast-paced and dynamic, with priorities and projects that can shift quickly
• Ownership-driven, expecting engineers to gather their own requirements and drive work end-to-end
• Data-heavy, working across dozens of heterogeneous sources and formats
• Global, coordinating daily with leadership and teams based in India and Santa Clara, California

About the Role

We are seeking a Senior Data Analytics Engineer to support one of Marvell’s business units as part of a broader initiative that will place two senior analytics engineers per unit (across 22 business units) so that engineers can back each other up. You will own the full data lifecycle for your unit: gathering requirements directly from business stakeholders, sourcing and ingesting the data you need, modeling it cleanly inside a Databricks-based data lake, and delivering finished, decision-ready reporting.

Data sources are highly heterogeneous — spanning SharePoint, Excel files, plain text files, and numerous internal systems feeding a centralized data lake in Databricks — and business domains range from highly technical (microchip engineering data) to operational (PMO project tracking, HR staffing and resource allocation). You will need to learn each domain’s data model largely on your own and translate it into something reliable and reportable.

What You’ll Do

• Gather requirements directly from business unit stakeholders and translate them into a working data pipeline, largely independently
• Design, build, and maintain end-to-end analytics pipelines: ingestion, transformation, and delivery
• Ingest and consolidate data from heterogeneous sources — SharePoint, Excel, text files, APIs, and other systems — into a centralized Databricks data lake
• Build and maintain ingestion and transformation jobs using Databricks bundles
• Model data cleanly (including dimensional/star-schema modeling) to support reliable, reusable reporting
• Build and maintain dashboards and reports in Power BI for weekly business delivery
• Continuously cycle requirements with stakeholders: validate data quality, refine existing reports, and identify new analytics opportunities
• Adapt to shifting priorities and occasional project reassignment, while remaining able to pick up support on prior work when needed
• Collaborate with a PMO function that coordinates deliverables, timelines, and definitions of done across the broader data program
• Communicate proactively and clearly with cross-functional stakeholders and leadership based in India and Santa Clara, California — strong, frequent communication is critical to success in this role
• Evaluate and responsibly apply AI-assisted tools to speed up data exploration, documentation, code generation, or quality checks within the bounds of the client’s security policies

Who You Are

• Highly proactive and comfortable operating with minimal day-to-day direction
• Strong, clear communicator — this role has a track record of turnover tied to communication gaps, so this is a critical trait, not a nice-to-have
• Comfortable in a fast-changing, sometimes unstructured environment where you may be reassigned between projects
• Curious and able to learn unfamiliar, domain-specific data models quickly (e.g., microchip engineering data)
• Analytical and detail-oriented, with strong end-to-end ownership instincts
• Effective working across time zones and cultures, including regular interaction with India-based leadership

Required Experience

• 5+ years of experience in Data Engineering / Analytics Engineering, at a senior level
• Strong, hands‑on SQL skills
• Hands‑on production experience with Databricks, including Databricks bundles for pipeline deployment
• Experience with Snowflake (a plus)
• Working familiarity with Apache Airflow (optional — high-level understanding is sufficient)
• Experience building reports and dashboards in Power BI
• Experience consolidating heterogeneous data sources (SharePoint, Excel, flat files, APIs, databases) into a unified data lake/lakehouse
• Experience with dimensional data modeling for analytics and reporting
• Demonstrated ability to work independently, end-to-end, from requirements gathering through delivery
• Practical familiarity with AI‑assisted tools for data/analytics work (e.g., AI coding assistants, LLM‑based data exploration or documentation tools) is a plus

Tech Stack / Tools

• Data Processing & Modeling:
  • SQL
  • Databricks (including Databricks bundles)
  • Dimensional / star-schema data modeling
• Data Platform & Sources:
  • Databricks-based data lake
  • Snowflake
  • SharePoint, Excel, and text-file data sources
• Orchestration:
  • Apache Airflow (optional / nice-to-have)
• Reporting & BI:
  • Power BI
• AI & Emerging Tools (nice to have):
  • Exposure to AI-assisted development or analytics tools (e.g., AI coding copilots, LLM-based data quality or documentation assistants)
  • Awareness of how AI/ML techniques can support anomaly detection, data quality checks, or automated insight generation
  • Comfortable being evaluated through an AI-powered video interview process as part of the selection workflow

Languages

English: High level required (C1 or above) — English is the mandatory, foundational working language for all communication and documentation in this role.

Working Conditions

• Client‑provided laptop with strict, monitored security controls; personal cloud storage or unauthorized file transfers are not permitted on client equipment
• Reports to leadership based in India and Santa Clara, California
• Occasional opportunity for a company‑funded trip to Marvell’s Santa Clara, California offices (approximately one to two people per quarter, coordinated through the consultancy)
• Selection process may include an AI-powered video interview (tool: Eco) evaluating technical strengths/weaknesses and English proficiency, followed by a technical interview with Marvell’s data architects

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