Data Engineering & Business Intelligence.

Configures data architectures, warehouses, marts, and reporting models for analytics and decisions

SeniorSoftware spend $7,800 / seat / yr (2026)129 contacts in the graph hold this role

Sample taken from the live graph on 7 October 2026; refreshed on demand.

What this role actually does

Responsibilities and pain points, sourced from the production graph.

Key responsibilities

Design and Operate Data Pipelines That Extract, Load, Transform, and Serve Governed Datasets for Analytics, Reporting, and AI Use Cases.
Maintain Data Warehouse and Lakehouse Structures That Balance Query Performance, Cost, Security, and Business-user Access.
Build Certified BI Dashboards, Semantic Models, and Recurring Reports That Executives and Operating Teams Use for Decisions.
Verify Data Accuracy, Freshness, Completeness, and Lineage Before Datasets Are Promoted Into Production Reporting.
Translate Stakeholder Questions Into Metric Definitions, Source-system Requirements, and Documented Dashboard Specifications.
Troubleshoot Failed Jobs, Broken Dashboards, and Upstream Schema Changes Within Agreed Data SLAs.

Software pain points

Dashboards break after upstream application teams rename columns or change event payloads without notice
Critical datasets miss freshness SLAs because orchestration failures are hidden across DAGs, warehouses, and BI refreshes
Multiple teams define the same metric differently across dbt models, spreadsheets, and BI dashboards
Manual connector maintenance consumes engineering time whenever SaaS APIs change or add fields
Warehouse bills rise after inefficient models, unbounded BI extracts, or poorly scheduled jobs hit production
Stakeholders do not trust reports because lineage, ownership, and last-refresh metadata are not visible

How this role buys

The categories they buy, the people who approve.

Top software categories

Columnar Database Software
Data Integration / ETL
Data Transformation
ETL Tools
Visitor Behavior Intelligence Software
Data Quality Tools

Decision makers

Telesales
Chief Data Officer
Engineering - Executive
Digital Forensics Engineering
Information / Cyber Security - Executive
Procurement

Workflow

A day in the workflow.

Starts day by checking failed DAGs and freshness alerts in Apache Airflow, Monte Carlo, and Slack; reviews Jira tickets and Confluence specs, then opens VS Code to edit SQL, Python, and dbt models against Snowflake or BigQuery; spends late morning validating joins, schema changes, and row counts with dbt Cloud, GitHub pull requests, and warehouse query history; joins a stakeholder sync in Zoom to reconcile metric definitions with product, finance, or sales leaders while updating the semantic notes in Confluence; uses Power BI, Tableau, or Looker after lunch to refresh dashboards and investigate ad hoc questions; closes by merging reviewed code in GitHub, watching Airflow runs, posting status in Slack, and logging unresolved data-quality risks in Jira

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