Livefront

Livefront

Since 2001, Livefront has helped companies grow by designing, building, and scaling digital products people love. Let's create something remarkable, together.

Internet Software & Services
11-50

Description

  • Design and build production pipelines using Lakeflow Declarative Pipelines, Autoloader, Structured Streaming, and Declarative Automation Bundles.
  • Architect Databricks Lakehouse solutions using medallion architecture, Delta Lake, and Unity Catalog.
  • Develop and maintain DLT, PySpark, and dbt transformation layers with data quality controls and SLAs.
  • Build 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 assess data challenges, define strategies, and implement sustainable solutions.
  • Design governed data-to-application architectures using Lakebase-backed services and Databricks Apps.
  • Champion governance, access control, lineage, compliance, observability, and data quality.
  • Contribute to Livefront’s Databricks practice through accelerators, enablement, certifications, and partner materials.

Requirements

  • 7–10 years of data engineering experience, including at least 5 years in production Databricks environments.
  • Production expertise with Lakeflow Declarative Pipelines, Autoloader, Structured Streaming, Lakeflow Jobs, and Unity Catalog.
  • Experience designing production-scale Lakehouse architectures, including medallion patterns, Delta Lake, partitioning, Z-ordering, and query optimization.
  • Proficiency in SQL and Python with strong data modeling, schema design, and query optimization skills.
  • Experience with pipeline testing, observability, data quality frameworks, Git, CI/CD, and Declarative Automation Bundles.
  • Working knowledge of AWS and Azure services for Databricks deployments; GCP familiarity preferred.
  • Strong communication and problem-solving skills, including the ability to explain complex concepts to varied stakeholders.
  • Experience with Kafka, Kinesis, alternative cloud data platforms, healthcare, or fintech is preferred.
  • MLOps/LLMOps experience with MLflow, Model Serving, Vector Search, Databricks Apps, or Lakebase is preferred.
  • Databricks or Apache Spark certification, experience with Java/Go/Scala, open-source contributions, or public technical education is preferred.

Benefits

  • Compensation of $150,000–$180,000.
  • Opportunities to work on high-impact products and accounts with broad reach.
  • Respectful, trust-based, egoless collaborative work environment.
  • Professional growth through mentoring, education, community involvement, and Databricks practice-building.
  • Fast-moving hiring process with a take-home exercise allotted up to one week.
  • Equal-opportunity evaluation based on merit, competence, and qualifications.

Interested in this position?

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