Senior Data Engineer, Databricks

Job not on LinkedIn

🕒 September 4

🇺🇸 United States – Remote

💵 $150k - $180k / year

⏰ Full Time

🟠 Senior

🚰 Data Engineer

👻 Ghost score 4%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Livefront

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2001

💼 Consulting

🤖 Artificial Intelligence

🤝 B2B

💰 Private equity on 2023-10

Consulting • Artificial Intelligence • B2B

Livefront is a digital product and engineering consultancy that builds AI-first products, platforms, and intelligent agents for enterprise clients. The firm combines strategy, product design, AI & automation, digital engineering, and data & analytics in a co-creative model (called MESH) to accelerate digital transformation and ship customer-facing software from discovery through scale. Livefront highlights work across healthcare, retail, automotive, and large enterprises—delivering AI companions, revenue-optimization agents, conversational/LLM integrations, platform engineering, and modern data pipelines to improve product outcomes and operational efficiency. The company positions itself as a partner-first, B2B services provider focused on measurable business impact.

📋 Description

• Design and build production data pipelines using Lakeflow Declarative Pipelines, Autoloader, and Structured Streaming • Own ingestion, transformation, data quality expectations, and CI/CD deployment via Declarative Automation Bundles • Architect and implement Databricks Lakehouse solutions using medallion architecture, Delta Lake, and Unity Catalog • Build and maintain DLT pipelines, PySpark notebooks, and dbt transformation layers with data quality constraints and SLAs • Design and maintain data and AI foundations using Unity Catalog, Feature Store, MLflow, and Model Serving • Collaborate with product and backend engineers on data models, APIs, and application data contracts • Consult with clients to understand data challenges, develop data strategies, and implement sustainable solutions • Lead client data architecture discussions or support internal teams with specialized data expertise • Work in AWS and Azure multi-cloud environments • Champion data governance, including access control, lineage, data quality policies, and compliance • Design data-to-application architectures involving Lakebase-backed services and Databricks Apps • Contribute to Livefront's Databricks practice, accelerators, internal enablement, certification goals, and partner go-to-market materials • Participate in a hiring process that may include a preliminary phone interview, video interviews, and a take-home exercise

🎯 Requirements

• 7-10 years of data engineering experience, including at least 5 years in production Databricks environments • Preferably experience in consulting or client delivery • Working knowledge of AWS and Azure services relevant to Databricks deployments, including storage, networking, IAM, and compute • Deep production-grade expertise with Lakeflow Declarative Pipelines, Autoloader, Structured Streaming, Lakeflow Jobs, and Unity Catalog • Experience designing production-scale Lakehouse architectures, including medallion patterns, Delta Lake table design, partitioning, Z-ordering, and query optimization • Experience with data pipeline testing, observability, and CI/CD, including unit testing, data quality frameworks, Git, and Declarative Automation Bundles • Strong proficiency in SQL and Python • Understanding of data modeling, schema design, and query optimization • Ability to explain complex data concepts to technical and non-technical stakeholders • Ability to navigate ambiguous requirements and deliver pragmatic solutions • Above-average discipline and personal organization • Comfort with critique and peer review • Hunger for personal and professional growth • Love and care for data engineering • Short note about yourself, summary of work experience, and link to actively maintained public profiles when applying

🏖️ Benefits

• Opportunity to work with passionate and talented people • Respectful, mutually trusting, egoless collaboration environment • Work on products and accounts with outsized impact and reach • Team reputation for excellence and community giving through educating, mentoring, and sponsoring • Opportunity to help build the Databricks practice from the ground up • Fast hiring process with video interviews and a take-home exercise of up to one week