We are looking for a Data Engineer who is certified in Databricks (required) to join our team. In this role you will be designing, developing, and optimizing scalable data pipelines and workflows on Databricks. The engineer will work closely with stakeholders to make certain data reliability, performance, and alignment with business requirements.

Scope of Work

Data Pipeline Development:

• Building efficient ETL/ELT pipelines using Databricks and Delta Lake for structured, semi-structured, and unstructured data.
• Transforming raw data into consumable datasets for analytics and machine learning.

Data Optimization:

• Improving performance by implementing best practices like partitioning, caching, and Delta Lake optimizations.
• Resolving bottlenecks and ensuring scalability.

Data Integration:

• Integrating data from various sources such as APIs, databases, and cloud storage systems (e.g., AWS S3, Azure Data Lake).

Real-Time Streaming:

• Designing and deploying real-time data streaming solutions using Databricks Structured Streaming.

Data Quality and Governance:

• Implementing data validation, schema enforcement, and monitoring to ensure high-quality data delivery.
• Using Unity CatLog to manage metadata, access permissions, and data lineage.

Collaboration and Documentation:

• Collaborating with data analysts, data scientists, and other stakeholders to meet business needs.
• Documenting pipelines, workflows, and technical solutions.

Responsibilities

Fully functional and documented data pipelines.

Optimized and scalable data workflows on Databricks.

Real-time streaming solutions integrated with downstream systems.

Detailed documentation for implemented solutions and best practices.

Skills and Qualifications

Proficiency in Databricks(certified), Spark, and Delta Lake.

Strong experience with Python, SQL, and ETL/ELT development.

Familiarity with real-time data processing and streaming.

Knowledge of cloud platforms (e.g., AWS, Azure, GCP).

Experience with data governance and tools like Unity CatLog.

Assumptions

Access to necessary datasets and cloud infrastructure will be provided.

Timely input and feedback from stakeholders.

Success Metrics

Data pipelines deliver accurate and consistent data.

Workflows meet performance benchmarks.

Real-time streaming solutions operate with minimal latency.

Stakeholders are satisfied with the quality and usability of the solutions.