Technical Data Analyst
Calico Energy United States · $80K–$105K/yr
Software Development · 11-50 employees
About the role
The Technical Data Analyst will serve as a subject matter expert for client data integrations, managing requirements scoping, mapping, and architecture planning. They will also collaborate with engineering and support teams to troubleshoot data issues and maintain data pipelines.
What they look for
Requirements
Candidates must have 3+ years of experience with enterprise software and data integrations, along with advanced proficiency in SQL and spreadsheet tools. Strong analytical, communication, and problem-solving skills are required to reconcile datasets and support software implementation projects.
Benefits
Full description
About the role
Technical Data Analyst | Full-Time, Salaried | $80k-$105k | Eligible for Benefits
Calico is a growing company looking for a Technical Data Analyst to support our Utilibridge™ application. This position will require expertise on the data processes, tools and formats that underpin our flagship software product and close collaboration with our engineering, product and support teams. We are specifically looking for someone excited to deepen their data and domain skills across an analysis-to-engineering spectrum. This position is responsible for data analysis, integration support, and related internal and external deliverables. This role will report to our Interim Head of Engineering.
Please note that this hiring process will include a synchronous assessment of technical skills.
What you'll do
- Serve as a subject matter expert and primary technical resource for new client data integrations by contributing to requirements scoping, data mapping, architecture planning, technical implementation, QA / validation. Work closely with Product Management and Engineering leads to execute new Utilibridge implementations and data integration projects effectively.
- Work closely with the Utilibridge Product Support team to resolve user data issues through troubleshooting, root cause investigation, escalation to client source data points of contact, and confirmation of data repair. Communicate clear status and next steps with internal and external stakeholders.
- Collaborate with software engineers on execution, maintenance and improvement of data pipelines, taking responsibility over time for some standard, recurring processes. Contribute to roadmap-driven revisions to the same.
Qualifications
Required Qualifications:
- 3+ years working with enterprise software enabled by data integrations or commensurate skill level
- Experience with data analysis, flows, formats, mapping, and standards
- Advanced proficiency in spreadsheets, including complex formulas, tables, data validation, and tools for combining and transforming data
- Demonstrated experience cleaning, reconciling, and quality-checking datasets from multiple sources and troubleshooting discrepancies
- Proficiency in SQL, including writing and troubleshooting queries that join, filter, aggregate, and validate data across multiple tables
- Demonstrated ability to translate business questions into analysis, investigate data-quality issues, and reconcile results against source data
- Experience documenting analyses for both internal and external audiences
- Experience working with software development teams
- Strong relationship building and communication skills, both written and oral
- Research and information synthesis skills
- The emotional intelligence to balance your ambition and drive with self-awareness and empathy
- The experience and confidence to work independently
Preferred Qualifications:
- Experience with utility data and the energy industry
- Experience with T-SQL and Microsoft SQL Server / Azure SQL
- Familiarity with ETL/ELT concepts, data pipelines, pipeline orchestration, and batch processing
- Experience working with sensitive data, such as PII, PCI, or HIPAA-regulated data
- Experience with command-line tools and scripting, such as Bash or PowerShell
- Experience with interactive data-analysis environments, such as JupyterLab or Jupyter Notebook
- Experience with modern data formats and analytical tools, such as Parquet and DuckDB
- Experience with data lakes or similar large-scale data storage environments
- Familiarity with containerized environments such as Docker or Kubernetes
- Familiarity with programming and version-control workflows
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