Databricks Developer

As a Databricks Developer, you will design and build the enterprise data pipelines that power analytics, reporting and AI initiatives for a leading company in the energy sector. Join a fully remote data engineering team working hands-on with cutting-edge Lakehouse technology., We are looking for a highly skilled Data Engineer with 5+ years of experience to design, build and optimize enterprise data pipelines on the Databricks Lakehouse platform for a leading energy sector company. In this role, you will be the hands-on technical driver responsible for transforming raw data into high-quality, actionable datasets. You will build and maintain a Medallion architecture, optimize Spark workloads, and ensure the data infrastructure seamlessly supports advanced analytics, BI dashboards and emerging Generative AI applications., Data Pipeline Engineering

• Design, build and maintain scalable, robust ETL/ELT pipelines using Python, SQL and Apache Spark within the Databricks environment.

• Implement and manage a robust Medallion architecture (Bronze, Silver, Gold layers) to process and refine data from diverse sources.

• Develop and maintain the Gold semantic layer specifically optimized for high-performance consumption by BI tools (e.g., Power BI).

Platform Optimization & Architecture

• Optimize Databricks workloads, cluster configurations and Spark queries to ensure high performance and cost efficiency.

• Work extensively with open table formats, specifically Delta Lake and Apache Iceberg, to ensure ACID compliance, time travel and efficient data storage.

• Execute complex data migrations, including transitioning legacy workloads from traditional cloud data warehouses (e.g., AWS Redshift) into the Databricks Lakehouse.

Data Governance & Automation

• Implement data governance and access control policies at the table, row and column levels using Databricks Unity Catalog.

• Automate deployment processes and pipeline orchestration using Databricks Workflows, CI/CD pipelines (e.g., GitHub Actions, Azure DevOps) and tools like Terraform.

• Embed data quality checks and monitoring directly into pipelines to ensure strict Master Data Management (MDM) standards are upheld.

AI & Advanced Analytics Support

• Collaborate closely with Data Scientists and AI Engineers to provision clean, structured data for machine learning model training and inference.

• Support the data foundations required for GenAI frameworks, autonomous agents and AI observability platforms.

• 5+ years of dedicated data engineering experience in an enterprise environment.

• Expert-level proficiency in Python and SQL.

• Extensive hands-on experience with Databricks, Apache Spark and Delta Lake.

• Strong understanding of distributed systems, big data architecture and data modeling techniques (e.g., Kimball, Data Vault).

• Deep familiarity with cloud-native data services (AWS, Azure or GCP), specifically cloud storage (S3/ADLS) and compute provisioning.

• Proven experience with version control (Git), CI/CD methodologies and agile software development life cycles.

NICE TO HAVE

• Experience evaluating and working with Apache Iceberg alongside Delta Lake.

• Familiarity with streaming data architectures (e.g., Structured Streaming, Kafka).

• Experience building backend frameworks or internal tools using lightweight libraries like Streamlit.

EDUCATION

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Engineering or a related field.

PREFERRED CERTIFICATIONS

• Databricks Certified Data Engineer Associate or Professional.

• AWS, Azure or GCP data/cloud certifications.