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Solutions Architect - Databricks

EXL Service -
United States

Job Details

Full-time

Qualifications

Data model design
Commercial use (data warehousing systems)
Databricks
Data Integration (Data management)
Requirements design
Spark
Data retrieval time improvement
Enterprise solutions implementation
Scalable systems
Managing data teams
Schema design
SQL
Team development
Data Architecture Design (Architecture design skills)
ETL process automation
Solution architecture design
Airflow
Requirements analysis
Leading team collaboration initiatives
Scalability
Kafka
Distributed computing
Business requirements
System tuning
Query management
Implementing IT solutions
Project stakeholder communication
Design (software development lifecycle)
Data analytics technologies
Dimensional modeling

Full Job Description

Job Description: We are seeking a highly skilled and hands-on Data Architect / Lead Data Engineer to design, build, and scale modern data platforms leveraging the Databricks Lakehouse architecture. This role combines deep technical expertise, architecture design, and client-facing leadership, with an emphasis on driving Databricks adoption across enterprise data ecosystems.

Responsibilities: Job Functions:

Partner with client stakeholders to define data platform strategy and establish Databricks Lakehouse architecture as the standard.
Design scalable, secure, and high-performance data architectures across batch and real-time processing.
Build and present reference architectures, solution blueprints, and demos to drive adoption and technical buy-in.
Translate complex business requirements into robust technical data solutions.
Design and implement scalable data pipelines using Databricks (PySpark, SQL).
Build and optimize data models, data marts, and medallion architecture layers (Bronze/Silver/Gold).
Develop and manage ETL/ELT pipelines, including CDC, incremental processing, and performance tuning.
Ensure data quality, observability, monitoring, and alerting across production workloads.
Work with large-scale distributed data systems (Spark, Kafka, etc.).
Lead and mentor a team of data engineers across multiple workstreams.
Conduct code reviews, enforce best practices, and create reusable frameworks/patterns.
Drive end-to-end solution delivery, including architecture, development, and production deployment.
Collaborate with cross-functional teams including analytics, BI, data science, and cloud engineering.
Manage stakeholder communication and provide technical thought leadership.

Expected work split:

50% Hands-on Technical: Data engineering, pipeline development, architecture implementation
50% Leadership & Client Engagement: Solution design, stakeholder management, team leadership

Qualifications: Qualifications (Required):

6-10 years of experience in data engineering, data architecture, or analytics engineering
Strong experience with Databricks ecosystem (Spark, SQL, PySpark, workflows)
Expertise in:
- Big Data Technologies (Spark, Kafka, distributed systems)
- Data Warehousing & Modeling (OLTP/OLAP, dimensional modeling, medallion architecture)
- ETL/ELT pipeline development and orchestration (Airflow or similar tools)
Advanced proficiency in SQL and Python (Scala/Java/R is a plus)
Experience designing and delivering enterprise-grade data architectures
Strong understanding of performance tuning, query optimization, and large-scale data processing
Proven ability to translate business needs into scalable technical solutions
Experience leading teams and working in client-facing environments
Excellent communication, problem-solving, and stakeholder management skills

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