Senior Data Engineer designing scalable Databricks Lakehouse architectures for Bits In Glass, an AI and automation consulting firm. Building governed pipelines, streaming solutions, and cloud data platforms for client modernization.
About the role
- Senior Data Engineer designing scalable Databricks Lakehouse architectures for Bits In Glass, an AI and automation consulting firm. Building streaming pipelines, modernizing data platforms, and advising clients.
Responsibilities
- Design and implement end-to-end data architectures, including data lakes, data warehouses, and analytics platforms
- Define scalable, secure, and performant data integration and transformation strategies
- Translate business requirements into technical solutions supporting analytics, reporting, and AI initiatives
- Develop data models, ETL/ELT pipelines, and frameworks for structured and unstructured data
- Provide technical leadership and mentorship to data engineers and developers
- Promote best practices in data management and governance
- Ensure compliance with data governance, security, and privacy standards across platforms
- Optimize existing data architectures and processes for improved performance and reliability
- Stay current with cloud data services and emerging technologies such as Databricks, Snowflake, Azure Synapse, and AWS Redshift
- Advise clients on architecture decisions and data modernization best practices
- Collaborate with business stakeholders, data engineers, and analytics teams to align solutions with client goals
Requirements
- 5+ years of experience in data architecture, data engineering, or analytics solution design
- Hands-on experience with data lake and warehouse technologies, including Databricks, Snowflake, Redshift, or Synapse
- Deep understanding of data modeling, data integration, and ETL/ELT design
- Proficiency in SQL and one or more programming languages, including PySpark/Python or Scala
- Experience with complex data transformations and optimization within Spark
- Solid understanding of data governance, security, and privacy best practices
- Experience designing, implementing, and optimizing large-scale ingestion pipelines using Databricks Autoloader
- Practical knowledge of building and managing reliable, self-managing ETL/ELT pipelines using Delta Live Tables
- Experience building high-throughput, low-latency streaming data ingestion solutions using Apache Kafka, Spark Structured Streaming, and Databricks Streaming
- Extensive experience applying and enforcing Medallion architecture within a Databricks environment
- Experience designing and implementing CI/CD pipelines for Databricks workflows, notebooks, and cluster configurations using tools such as Azure DevOps, GitHub Actions, or GitLab CI
- Experience planning and executing data migration projects from traditional data warehouses into the Databricks Lakehouse
- Strong working knowledge of AWS or Azure data storage, networking, and security concepts relevant to Databricks deployment
- Ability to engage with clients, present technical solutions, and communicate complex ideas clearly
- Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or a related field
- Excellent problem-solving, communication, and collaboration skills
Benefits
- Supportive, collaborative, driven team environment
- Great Place to Work recognition
- Opportunities to deepen expertise and grow skills
- Meaningful work solving real-world business challenges
- Technical leadership and mentorship opportunities
- Work with modern cloud, data, and AI technologies
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