Sr Data Engineer - Databricks

CGI•Miami, FL

1d•Hybrid

About The Position

We're looking for a Data Engineer who thrives on designing scalable data systems and turning complex data into high-quality, consumable assets. If you're passionate about Databricks, Spark, and building robust pipelines that power real business decisions, this role puts you right at the center of it. You'll play a critical role in shaping our data ecosystem, engineering high-performance ETL/ELT pipelines, designing clean and reliable data models, and enabling seamless data access through APIs. This is not just pipeline work, this is about building production-grade data products that fuel analytics, reporting, and advanced data science.

Requirements

5+ years of experience in data engineering or related roles
Strong hands-on experience with Databricks and Apache Spark
Advanced SQL proficiency (complex joins, window functions, performance tuning)
Experience building and managing ETL/ELT pipelines
Experience configuring and supporting data exposure via APIs
Solid understanding of data warehousing and analytics concepts
Strong problem solving and analytical skills
Ability to work independently on complex data problems
Clear communication with both technical and non-technical stakeholders
Comfortable working in an agile / product-oriented environment
Excellent communication (verbal and written) skills to work effectively with cross-functional teams.
Ability to document processes and frameworks clearly for future reference.
Timeliness, accuracy, and professionalism
Self-starter and capable of working without close supervision.

Nice To Haves

Cloud Data Platform Design
Data Engineering
Databricks
ETL

Responsibilities

Design, develop, and maintain ETL/ELT pipelines using Databricks (Spark) for batch and incremental data processing
Implement robust data ingestion patterns from multiple source systems (files, databases, APIs, streaming where applicable)
Optimize Spark jobs for performance, scalability, and cost efficiency
Ensure data quality, reconciliation, and observability across pipelines
Develop complex SQL transformations for data cleansing, enrichment, and aggregation
Design and maintain analytical data models (fact/dimension, curated layers, reporting views)
Support downstream reporting, analytics, and data science use cases with well-structured datasets
Configure and manage data exposure through APIs for internal and external consumers
Partner with application and integration teams to define API contracts and data payloads
Ensure secure, scalable, and performance data access patterns
Support versioning and backward compatibility for published data services