Data Engineer

The ideal candidate thrives in an agile, collaborative environment and brings experience (or strong foundational knowledge, for emerging engineers) in building data pipelines and architectures using Databricks and modern data engineering frameworks.

Analytica has been recognized by Inc. Magazine as one of the fastest-growing private companies in the U.S. for three consecutive years. We partner with organizations across industries to deliver impactful, data-driven solutions. We offer competitive compensation, performance-based bonuses, employer-sponsored health benefits, professional development funding, and a 401(k) match.

Responsibilities include (but are not limited to):

• Design, build, and maintain scalable data pipelines and data architectures using Databricks and distributed processing frameworks (ownership and complexity vary by level)
• Develop and optimize data models, including dimensional (star/snowflake) schemas, to support analytics and reporting use cases
• Build and support ETL/ELT processes using SQL, Python, and Spark to ingest, transform, and curate datasets
• Collaborate with stakeholders to translate business requirements into technical data solutions
• Ensure data quality, integrity, and performance through testing, monitoring, and optimization
• Contribute to data platform design decisions, tooling selection, and engineering best practices (increasing influence at higher levels)
• Implement and support CI/CD pipelines for data workflows and assist with deployment and maintenance of data applications
• Apply data governance, security, and metadata management best practices

Qualifications (aligned to expertise level):

• Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related quantitative field (or equivalent experience)
• Experience working across cloud (AWS, Azure) or hybrid data environments 
• Emerging / Junior Data Engineer:
  • Foundational knowledge of SQL and Python (or similar language)
  • Exposure to Databricks, Apache Spark, or distributed data processing concepts through coursework, internships, or projects
  • Understanding of basic data modeling and ETL/ELT concepts
  • Ability to work within established pipelines and contribute to data workflows with guidance
• Mid-Level Data Engineer:
  • Hands-on experience building and maintaining data pipelines using Databricks and Spark
  • Strong SQL and programming skills (Python and/or Scala)
  • Experience designing data models and optimizing data processing workflows
  • Ability to independently deliver data solutions and troubleshoot performance issues
• Senior Data Engineer:
  • Deep expertise in Databricks, Spark, and large-scale data processing architectures
  • Proven ability to design and implement scalable data platforms and lakehouse architectures
  • Strong experience with performance tuning, data reliability, and cost optimization
  • Ability to lead technical design, mentor team members, and influence data engineering best practices
  • Experience working cross-functionally to define data strategy and architecture
• Strong communication skills and ability to work effectively with both technical and non-technical stakeholders
• US Citizenship and must be able to obtain and maintain security clearance

Preferred Qualifications:

• Experience with Delta Lake and lakehouse architecture pattern
• Familiarity with modern data stack tools (e.g., dbt, Airflow, or similar orchestration tools)
• Experience with data governance, cataloging, and lineage tools
• Experience mentoring junior engineers or leading technical initiatives (senior level)
• Relevant certifications in data engineering or big data technologies particularly in Databricks