Data Engineer

SMBC•New York City, NY

11d•Hybrid

About The Position

We are seeking a Databricks Engineer with AWS expertise to build, optimize, and maintain our enterprise data Lakehouse infrastructure. In this role, you will be responsible for designing high-performance data pipelines, implementing regulatory and real time reporting along with advanced analytics environments, and ensuring seamless integration between Databricks and core AWS services to support real-time financial trading data consumption.

Requirements

Professional experience in architectural design and development within the Databricks platform, working in an AWS cloud environment.
Proven proficiency in automated deployments using Databricks Asset Bundles (DABs), Terraform (specifically the Databricks and AWS providers), and standard Git pipelines (e.g., GitHub Actions, GitLab CI/CD, or AWS CodePipeline).
Programming skills in Python (PySpark) and SQL for complex data manipulation and transformation.
Strong understanding of Apache Spark internals, Delta Lake mechanics, and streaming data concepts (e.g., interacting with Amazon MSK or Kafka data streams).
Proven experience building production-grade ETL/ELT pipelines, handling data schema validation, and cleansing raw capture feeds.

Nice To Haves

Databricks Certified Data Engineer Professional or AWS Certified Data Engineer – Professional is highly advantageous.

Responsibilities

Design and implement robust data pipelines using the Databricks Medallion Architecture (Bronze, Silver, Gold layers) to process structured and unstructured data.
Develop, scale, and orchestrate complex data workflows utilizing Databricks Jobs and Delta Live Tables (DLT).
Standardize and automate the deployment of Databricks assets, workspace configurations, and code pipelines across Dev, QA, and Production environments.
Ensure seamless data cataloging, storage, and movement across the AWS ecosystem, specifically integrating Databricks with Amazon S3, AWS Glue, and AWS IAM for secure access control.
Tune Spark clusters, optimize Delta Lake storage (e.g., Z-Ordering, partitioning), and manage compute costs within the AWS environment.
Implement fine-grained data access controls, data lineage, and auditing using Unity Catalog or native cloud security controls.
Partner with Data Scientists, Risk Managers, and downstream analytics teams to deliver clean, business-ready data views for reporting and AI modeling.