Business Intelligence Analyst & Data Engineer- Remote

DeepSource Technologies; United States; Hybrid; USD 90,000 - 130,000; Full time

Job summary

DeepSource Technologies is seeking a hybrid Business Intelligence Analyst & Data Engineer to own the full data lifecycle. You will design pipelines, build dashboards, and enable AI-driven analytics across the organization.

This role blends data engineering rigor with storytelling and stakeholder collaboration, delivering end-to-end data products and governance for trusted insights.

Qualifications

• 3+ years in data engineering, BI analytics, or analytics engineering.
• Experience designing and maintaining ETL/ELT pipelines.
• Strong SQL skills with complex queries and large datasets.
• Experience with BI tools like Tableau/Power BI/Looker.
• Understanding of star/snowflake schemas and data modeling.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines integrating data from multiple sources into centralized warehouses.
• Ensure data quality and integrity across pipelines through validation and monitoring.
• Optimize data models for reporting performance and scalability.
• Build and maintain dashboards and reports translating data into insights.
• Collaborate with stakeholders to gather reporting requirements and define metrics.
• Document data lineage, logic, and calculation methods.

Skills

SQL

ETL/ELT pipelines

Data modeling

BI dashboards

Cloud data warehouses

AI/LLM tooling

Education

Bachelor’s degree in Computer Science, Data Science, Information Systems, Statistics, or related field

Tools

Fivetran

dbt

Airflow

Tableau

Power BI

Looker

Snowflake

BigQuery

Redshift

Job description

We’re looking for a hybrid Business Intelligence Analyst & Data Engineer who can operate across the full data lifecycle — from building and maintaining reliable data pipelines to designing dashboards and reports that drive business decisions. This role sits at the intersection of data infrastructure and analytics: you’ll own how data flows into our systems and how it’s ultimately consumed by stakeholders across the organization.

This is a great fit for someone who enjoys both the technical rigor of data engineering and the storytelling/analytical side of BI work, and who wants ownership over an end-to-end data product rather than sitting in just one lane. AI-driven analytics is a growing part of our roadmap, so we’re especially interested in candidates who are curious about applying AI/LLM tools to make data more accessible and self-service across the business.

Key Responsibilities

Data Engineering

• Design, build, and maintain ETL/ELT pipelines that integrate data from multiple source systems (CRM, ERP, project management tools, spreadsheets, APIs) into centralized data warehouses or data marts
• Ensure data quality, consistency, and integrity across pipelines through validation, testing, and monitoring
• Optimize data models (star/snowflake schemas) for reporting performance
• Automate manual data processes and reduce reliance on ad hoc spreadsheet work
• Manage data source connections, credentials, and refresh schedules
• Troubleshoot and resolve data pipeline failures, latency issues, and discrepancies

Business Intelligence & Analytics

• Build and maintain dashboards and reports (e.g., in Tableau, Power BI, or similar) that translate raw data into clear, actionable insights
• Partner with business stakeholders to gather reporting requirements and translate them into technical specifications
• Define and maintain a single source of truth for key business metrics (a data dictionary / metrics glossary)
• Conduct ad hoc analysis to answer business questions and support strategic decision-making
• Monitor dashboard usage and proactively improve reports based on stakeholder feedback
• Document data lineage, business logic, and calculation methodology for transparency and auditability

AI-Enabled Analytics

• Design and maintain data structures (clean schemas, semantic layers, metric definitions) that are well-suited for consumption by AI agents and natural-language query tools, not just human-facing dashboards
• Build or support AI-powered reporting assistants/chatbots that let stakeholders ask business questions in plain language and get accurate, governed answers
• Evaluate and integrate LLM-based tools into the analytics stack (e.g., automated insight generation, anomaly detection, natural-language-to-SQL interfaces)
• Establish guardrails for AI-generated analysis — certified data sources, citation of underlying data, and validation processes to ensure trustworthy outputs
• Stay current on AI/BI tooling trends and proactively recommend where automation or AI can reduce manual reporting work

Cross-Functional Collaboration

• Work closely with department leaders (Sales, Finance, Operations, Professional Services, etc.) to understand data needs and pain points

• Act as a technical liaison between business teams and IT/engineering on data-related initiatives

• Support the rollout of new tools, data sources, or automation initiatives (e.g., AI-powered reporting agents, workflow automation platforms)

• Bachelor’s degree in Computer Science, Data Science, Information Systems, Statistics, or a related field (or equivalent practical experience)

• 3+ years of experience in a data engineering, BI analyst, or analytics engineering role

• Strong SQL skills, including experience writing complex queries, optimizing performance, and working with large datasets

• Experience building and maintaining ETL/ELT pipelines (custom scripts, or tools like Fivetran, dbt, Airflow, Workato, or similar)

• Hands-on experience with a BI/visualization tool (Tableau, Power BI, Looker, or similar)

• Solid understanding of data modeling concepts (dimensional modeling, normalization, star schemas)

• Experience working with cloud data warehouses (Snowflake, BigQuery, Redshift, or similar)

• Strong analytical mindset with the ability to translate ambiguous business questions into structured data analysis

• Excellent communication skills — able to explain technical concepts to non-technical stakeholders

Preferred Qualifications

• Experience with Python or another scripting language for data manipulation and automation
• Familiarity with CRM/ERP systems (Salesforce, NetSuite) and how operational data flows from them
• Experience with API integrations and workflow automation platforms (Workato, Zapier, or similar)
• Hands-on experience building or integrating AI/LLM-powered tools (e.g., Claude, ChatGPT, Copilot) into data workflows — such as natural-language querying, automated report generation, or AI-driven data agents
• Familiarity with prompt engineering, retrieval-augmented generation (RAG), or agent-based architectures as applied to analytics
• Experience establishing data governance practices (certified data sources, metric definitions, documentation standards) suitable for both human and AI consumers
• Prior experience in a Professional Services, Finance, or Operations analytics function