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Open nowPosted yesterday

Databricks Data Engineer

EXL2,299 open roles

Where
New York, United States
Work mode
Hybrid
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Your applicationOpen nowDatabricks Data EngineerEXL · New York, United States

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The clock on this job

Early applications get read.

13.5% of postings close within 7 days. Measured by our own scanner across the market. EXL postings stay open a median of 21 days.

Share of postings closed within
  1. 2.3%1 day
  2. 4.6%3 days
  3. 13.5%7 days
  4. 21.8%14 days
  5. 36.6%30 days
  6. 58.9%60 days
This job: posted yesterday

EXL median: 21 days open

The posting

Job Description

Job Title: Databricks Data Engineer Experience: 8-10 Years Location: US Employment Type: Full-Time

Job Summary

We are looking for a highly experienced Databricks Data Engineer with 8-10 years of experience in Data Engineering, Big Data, and Cloud Data Platforms. The ideal candidate will lead the design, development, modernization, and optimization of enterprise-scale data platforms using Databricks Lakehouse architecture. This role requires strong technical leadership, solution architecture expertise, stakeholder management skills, and hands-on experience building scalable cloud-native data ecosystems.

The candidate will be responsible for defining data engineering standards, mentoring teams, driving modernization initiatives, and delivering high-performance data solutions that support analytics, AI/ML, and business intelligence initiatives.

Key Responsibilities Data Engineering & Architecture

  • Design and implement enterprise-scale data platforms using Databricks Lakehouse architecture.
  • Architect and develop scalable ETL/ELT data pipelines for batch and real-time processing.
  • Define enterprise data models, ingestion frameworks, and transformation strategies.
  • Lead the implementation of Delta Lake, Unity Catalog, and data governance frameworks.
  • Build and optimize highly scalable Spark and PySpark solutions handling large-volume datasets.
  • Drive cloud data platform modernization and migration initiatives.

Technical Leadership

  • Serve as the technical lead for Databricks and Data Engineering programs.
  • Establish engineering best practices, coding standards, and architecture guidelines.
  • Conduct architecture reviews, code reviews, and solution assessments.
  • Mentor and guide Data Engineers, Technical Leads, and junior team members.
  • Collaborate with Enterprise Architects, Product Owners, Data Scientists, and Business Stakeholders.

Cloud & Platform Engineering

  • Design cloud-native data solutions on Azure, AWS, or GCP.
  • Implement secure, scalable, and resilient data architectures.
  • Optimize costs, performance, reliability, and availability of data platforms.
  • Lead CI/CD and Infrastructure-as-Code implementation for data workloads.
  • Define and implement monitoring, observability, and operational excellence practices.

Governance & Data Quality

  • Establish data governance, lineage, cataloging, and security controls.
  • Ensure compliance with organizational and regulatory requirements.
  • Implement data quality frameworks and monitoring solutions.
  • Collaborate with security and compliance teams to enforce data protection standards.

Stakeholder & Delivery Management

  • Partner with business leaders to understand strategic data requirements.
  • Translate business requirements into scalable technical solutions.
  • Drive delivery planning, risk management, and resource coordination.
  • Present architecture decisions and technical recommendations to leadership teams.

Required Technical Skills Databricks

  • Databricks Lakehouse Platform
  • Databricks Workflows
  • Delta Lake
  • Unity Catalog
  • Structured Streaming
  • Databricks SQL
  • Delta Live Tables (DLT)
  • Databricks Asset Bundles

Big Data Technologies

  • Apache Spark
  • PySpark
  • Spark SQL
  • Kafka
  • Real-Time Streaming Architectures
  • Data Lake & Lakehouse Architectures

Cloud Platforms (Strong Experience in One or More) Azure

  • Azure Databricks
  • Azure Data Factory (ADF)
  • ADLS Gen2
  • Azure Synapse Analytics
  • Azure Key Vault
  • Event Hub

AWS

  • S3
  • EMR
  • Glue
  • Redshift
  • Lambda
  • IAM

GCP

  • BigQuery
  • Cloud Storage
  • Dataflow
  • Pub/Sub
  • Composer

Programming

  • Python
  • SQL
  • PySpark
  • Scala (Preferred)
  • Shell Scripting

DevOps & Automation

  • Git
  • Azure DevOps
  • GitHub Actions
  • Jenkins
  • Terraform
  • Docker
  • Kubernetes

Data Warehousing

  • Dimensional Modeling
  • Star & Snowflake Schema
  • Data Vault
  • Master Data Management
  • Data Governance Frameworks

Required Experience

  • 12-15 years of overall IT experience.
  • Minimum 6+ years of hands-on Databricks experience.
  • 7+ years of experience in Spark/PySpark-based data engineering.
  • Experience designing enterprise-scale Data Lake and Lakehouse solutions.
  • Strong experience leading large data transformation and modernization programs.
  • Experience handling structured, semi-structured, and unstructured data processing.
  • Experience supporting analytics, AI/ML, and reporting ecosystems.
  • Proven experience managing technical teams and driving architectural decisions.

Preferred Qualifications

  • Bachelor's or Master's Degree in Computer Science, Engineering, Information Technology, or related field.
  • Databricks Certified Data Engineer Professional.
  • Azure Data Engineer Associate.
  • AWS Data Analytics Specialty.
  • Google Professional Data Engineer Certification.
  • TOGAF or Cloud Architecture certifications are a plus.

Nice to Have

  • MLOps and AI Engineering exposure.
  • Snowflake and Databricks integration experience.
  • Microsoft Fabric exposure.
  • Data Mesh implementation experience.
  • Data Governance tools such as Collibra, Alation, or Informatica.
  • Expertise in Data Quality frameworks such as Great Expectations or Deequ.

Key Competencies

  • Solution Architecture
  • Technical Leadership
  • Stakeholder Management
  • Program Delivery
  • Data Strategy & Governance
  • Team Mentoring & Coaching
  • Problem Solving & Decision Making
  • Communication & Executive Presentation Skills
  • Performance Optimization
  • Innovation & Continuous Improvement

Technology Classification

Technology Tower: Data & AI Practice Area: Data Engineering Primary Skill: Databricks Lakehouse Engineering Secondary Skills: Apache Spark, PySpark, Cloud Data Platforms (Azure/AWS/GCP), Data Architecture, Data Governance, MLOps.

Responsibilities

Design and implement data pipelines, Optimize data processing and storage, Ensure data solutions meet performance standards, Provide technical support, Collaborate with stakeholders.

Qualifications

Bachelor's/Master's in Engineering 5-8 years

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