Overview We are seeking an experienced Data Engineer to design, develop, and support scalable data solutions that enable analytics, reporting, and operational data services across the organization. This role will focus on building and optimizing ETL/ELT pipelines using Databricks and Apache Spark, developing complex SQL transformations, and enabling secure and scalable data exposure through APIs. The ideal candidate brings strong hands-on technical expertise, a deep understanding of data engineering best practices, and the ability to work collaboratively across technical and business teams in an agile environment. Responsibilities:
Design, develop, and maintain ETL/ELT pipelines using Databricks and Apache Spark for batch and incremental data processing
Implement robust data ingestion patterns from multiple source systems including files, databases, APIs, and streaming sources where applicable
Optimize Spark jobs and data pipelines for performance, scalability, reliability, and cost efficiency
Ensure data quality, reconciliation, monitoring, and observability across pipelines and datasets
Develop advanced SQL transformations for data cleansing, enrichment, aggregation, and reporting
Design and maintain analytical data models including fact and dimension tables, curated data layers, and reporting views
Support downstream reporting, analytics, and data science use cases with well-structured and scalable 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 performant data access patterns
Support API versioning and backward compatibility for published data services
Collaborate with cross-functional teams to understand business and technical data requirements
Participate in agile delivery processes and contribute to continuous improvement initiatives
Document technical designs, processes, and operational procedures
Requirements:
5+ years of experience in data engineering roles
Strong hands-on experience with Databricks and Apache Spark
Advanced SQL proficiency including complex joins, window functions, and performance tuning
Experience building and managing ETL/ELT pipelines
Experience configuring and supporting data exposure through APIs
Solid understanding of data warehousing and analytics concepts
Experience with cloud data platforms such as AWS, Azure, or GCP preferred
Familiarity with CI/CD practices for data pipelines preferred
Experience with orchestration tools such as Airflow or Databricks Workflows preferred
Knowledge of data quality, monitoring, and reconciliation frameworks preferred
Strong problem-solving and analytical skills
Ability to work independently on complex data challenges
Clear communication skills with both technical and non-technical stakeholders
Comfortable working in an agile and product-oriented environment