Sr. Data Engineer
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Key skills for this role
About the Role
About Algebra Algebra builds and operates AI-powered workflows for mid-market companies as a managed service. We identify high-value operational processes, design AI systems to run them, and own the outcome end to end.
Key Skills for This Role
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About Algebra
Algebra builds and operates AI-powered workflows for mid-market companies as a managed service.
We identify high-value operational processes, design AI systems to run them, and own the outcome end to end.
We are not consultants.
We do not sell software licenses.
We are the accountable operator.
The Role
This is a senior data engineering role for someone who can turn messy business data into the foundation for reliable AI workflows.
Algebra’s agents are only as good as the data layer underneath them.
You will build the pipelines, models, integrations, and data infrastructure that allow our systems to understand client operations, surface the right context, and take action inside real business processes.
You need to be strong enough technically to design the data architecture, but practical enough to work inside imperfect client environments.
You should be comfortable moving between data pipelines, warehousing, APIs, orchestration, transformation logic, data quality, governance, and production debugging.
This is a builder role.
You will help define the data patterns, standards, and infrastructure Algebra uses as it scales from custom client deployments into repeatable systems.
If your instinct is to find the truth inside messy data, structure it properly, and make it useful for systems that actually run the business, this is the role.
Data Infrastructure and Pipelines
- Build production-grade data pipelines that support Algebra’s AI-powered workflows
- Design data ingestion patterns across client systems, APIs, databases, spreadsheets, documents, and third-party tools
- Build transformation logic that turns fragmented operational data into usable workflow context
- Own data movement across staging, production, and client-specific environments
- Set up orchestration, scheduling, monitoring, and failure handling for critical data flows
- Create reusable data pipeline patterns for repeatable client deployments
Data Modeling and Workflow Context
- Design data models that represent real business workflows, entities, rules, and operational states
- Build the structured data layer that allows AI agents to reason over client operations
- Translate messy business processes into clean schemas, relationships, and workflow-ready data structures
- Support use cases such as document intake, reconciliation, approvals, reporting, exception handling, and operational alerts
- Work closely with application and AI engineers to make sure data is usable inside actual product experiences
- Build systems that preserve context, lineage, and auditability where required
Integrations and Data Sources
- Connect to client systems, CRMs, ERPs, databases, finance tools, communication platforms, and document repositories
- Build reliable API integrations, sync jobs, webhooks, and ingestion workflows
- Handle authentication, permissions, data mapping, schema changes, and integration failure states
- Work with unstructured and semi-structured data, including PDFs, documents, emails, spreadsheets, and extracted fields
- Debug production data issues across pipelines, APIs, databases, and client systems
- Make data integrations repeatable instead of rebuilding every connection from scratch
Data Quality, Governance, and Reliability
- Build checks for data quality, completeness, freshness, duplication, and consistency
- Set up monitoring and alerting for data pipeline failures and data reliability issues
- Create practical data governance standards for client deployments
- Support secure handling of sensitive operational, financial, and client data
- Maintain clear data lineage, audit trails, and access control where needed
What We’re Looking For
- 10+ years of experience in data engineering, analytics engineering, platform data engineering, or backend data systems
- You have built and operated production data pipelines used by real systems, not just dashboards
- Strong experience with SQL, data modeling, ETL/ELT, orchestration, APIs, and cloud data infrastructure
- Experience with modern data warehouses, databases, and transformation frameworks
- You understand how to work with messy operational data, not only clean analytics datasets
- You can design data models that reflect real business entities, processes, rules, and exceptions
- You are comfortable working with structured, semi-structured, and unstructured data
- You understand data quality, lineage, access control, monitoring, and production reliability
- You can debug complex issues across source systems, pipelines, transformations, APIs, and application logic
Bonus Points
- You have built data infrastructure for AI, automation, LLM, or agent-based systems
- You have experience with vector databases, embeddings, retrieval systems, or knowledge graphs
- You have worked with document processing, OCR, data extraction, reconciliation, or human-in-the-loop review workflows
- You have built integrations with systems like Salesforce, HubSpot, Zoho, Microsoft 365, Google Workspace, Slack, Teams, SharePoint, NetSuite, SAP, Oracle, or finance/ERP tools
- You have experience with dbt, Airflow, Dagster, Prefect, Kafka, Snowflake, BigQuery, Redshift, Postgres, or similar tools
- You understand multi-tenant data architecture, role-based access control, audit logs, and client-specific data separation
What This Role Is Not
This is not a reporting or dashboard-only role.
The data layer you build will power real AI workflows and operational systems.
This is not a clean-room analytics role where every source is documented and every table is reliable.
You will work with messy, incomplete, inconsistent client data.
This is not a role where someone else defines the data architecture and you only implement tickets.
You will be expected to form a view and create the standards Algebra scales on.
If your best work requires a mature data platform, a large team, and stable requirements, this is probably not the right fit.
If you want to build the data backbone of an AI operations company from the ground up, we want to talk.
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