Databricks Engineer

Role: Databrick Eng with strong experience on core databricks, Pyspark, Python, Spark and scala

Location: Atlanta, GA Hybrid Onsite ( Only Local )

Duration: Contract

Job Description
Lead the design, development, and evolution of enterprise-grade data platforms and pipelines supporting credit risk products, decisioning capabilities, analytics, and regulatory reporting.
Architect and implement scalable ETL/ELT frameworks using Databricks, Apache Spark, Delta Lake, and cloud-native technologies to support high-volume data workloads.
Drive data platform modernization and cloud transformation, including the migration of legacy data assets to modern cloud-based and Data Lakehouse architectures.
Establish and advance data quality, lineage, metadata, governance, observability, and monitoring capabilities to deliver trusted, compliant, and reliable data products.
Partner across Risk, Product, Data Science, Architecture, Engineering, and Business teams to translate complex requirements into scalable, sustainable technical solutions.
Define and promote engineering standards and best practices across coding, testing, data quality, deployment automation, documentation, and operational excellence.
Lead technical design reviews and influence architectural decisions for data-intensive applications, platforms, and services.
Optimize large-scale data workloads and platforms for performance, reliability, scalability, resiliency, and cost efficiency.
Build reusable, governed data products and capabilities that enable AI, machine learning, advanced analytics, and emerging risk use cases.
Drive technical initiatives from strategy through execution, identifying dependencies, managing technical risks, and ensuring solutions align with business priorities and long-term platform strategy.
Mentor and coach engineers across the organization, providing technical guidance, design expertise, knowledge sharing, and feedback to strengthen engineering capabilities.
Shape technology roadmaps and delivery priorities across the ECR portfolio, balancing business value, engineering feasibility, risk, compliance, and long-term platform sustainability.
Ensure engineering solutions meet security, privacy, regulatory, compliance, and audit requirements through appropriate controls, documentation, and engineering practices.
Create clarity in complex and ambiguous environments, communicate technical trade-offs to senior stakeholders, and foster a culture of ownership, accountability, and continuous improvement.

All About You:
Deep expertise in designing and implementing large-scale data engineering solutions and distributed data processing systems.
Advanced proficiency with Databricks, Apache Spark/PySpark, Delta Lake, SQL, and Python.
Experience building and operating cloud-based data platforms using Azure, AWS, or GCP.
Strong experience developing enterprise-grade ETL/ELT pipelines, data integration frameworks, and streaming architectures.
Strong understanding of data modeling and data architecture for analytical and operational workloads.
Experience implementing data quality, lineage, metadata management, observability, and data governance frameworks in enterprise environments.
Experience working with modern data formats and storage technologies, including Parquet, Avro, and ORC.
Working knowledge of CI/CD, infrastructure-as-code, automated testing, Git/GitHub, and DevOps practices for data engineering environments.
Experience with workflow orchestration tools, such as Apache Airflow.
Strong understanding of security, privacy, regulatory, and compliance requirements associated with sensitive financial and customer data.
Proven ability to lead complex technical initiatives across multiple teams and influence engineering direction without direct authority.
Demonstrated ability to mentor engineers and elevate technical capability, including providing design guidance, knowledge sharing, and constructive feedback.
Excellent communication and stakeholder management skills, with the ability to clearly articulate technical concepts, trade-offs, risks, dependencies, and recommendations to both technical and business audiences.
Experience with Java-based application development is a strong plus, particularly in environments where data platforms integrate with enterprise applications and services.

Preferred Qualifications:
Bachelor's degree in Computer Science, Engineering, Information Systems, or a related STEM discipline. Equivalent practical experience will also be considered.
Preferred experience with:
o Databricks
o Apache Spark / PySpark
o Hadoop
o Delta Lake
o SQL and Python
o Apache Airflow
o Azure Data Services
o Kafka / event streaming
o GitHub and CI/CD tooling
o Cloud-based Data Lakehouse architectures