Full-Time Databricks Developer
Descripción del empleo
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.
HIGH-IMPACT DATA PROJECTS
LATEST LAKEHOUSE TECH
FULLY REMOTE
LEARNING & GROWTH
Don’t tick every box? If you meet around 70% of the requirements above, we’d still encourage you to apply.
ABOUT THE ROLE
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.
KEY RESPONSIBILITIES
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.
REQUIRED SKILLS & EXPERIENCE
- 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.
WORKING MODEL
Fully remote position.
13 Visitas totales, 2 Hoy