Lead Developer

Finoit Inc; Chicago (IL); On-site; USD 140,000 - 190,000; Full time

Job summary

Finoit Inc in Chicago seeks a senior data engineer with strong Microsoft Fabric and Azure experience to lead production data pipelines and AI/ML initiatives.

You will design scalable data platforms, integrate enterprise systems (Tamarac, eMoney, Salesforce FSC), implement data quality controls, and own reliability and SLAs.

Qualifications

• 7+ years hands-on data engineering experience.
• Bachelor’s degree in CS, IS, Data Eng, or related field (or equivalent).
• Deep Microsoft Azure experience (required).
• Proven production data pipelines integrating enterprise platforms (e.g., Tamarac, eMoney, Salesforce FSC).
• Strong SQL & Python; Spark/PySpark valued.
• Experience with RAG, prompt engineering, multi-agent architectures, memory patterns, semantic search/vector retrieval.
• Real production experience building data pipelines end-to-end in Microsoft Fabric.
• 1.5–2+ years applied AI/advanced analytics engineering.

Responsibilities

• Design, build, and maintain scalable data pipelines integrating wealth tech platforms into the enterprise data platform.
• Develop ingestion patterns: APIs, SFTP/file drops, vendor extracts, streaming feeds, replication.
• Model and curate conformed datasets to enable cross-platform reporting and dashboards.
• Implement data quality controls, monitoring, SLAs for reliable downstream delivery.
• Establish SLAs and monitoring frameworks for high reliability of enterprise data assets.
• Collaborate with stakeholders to translate requirements into technical designs and delivery plans.
• Document integration standards, reusable templates, and runbooks to improve maintainability.
• Implement automated testing and validation for data pipelines.

Skills

SQL

Python

Spark / PySpark

Data pipelines

Azure / Microsoft Fabric

AI/ML engineering

RAG / retrieval augmented generation

Model routing / agent frameworks

Education

Bachelor’s degree in Computer Science / Information Systems / Data Engineering or related field

Tools

Microsoft Fabric

Azure

Tamarac

eMoney

Salesforce FSC

Tax planning software

AI notetaking tools

Job description

Need very strong Microsoft fabric experience. Python. This is more of a AI/ML engineer. Large enterprise data.

This is a senior role. We’re very intentional about what “qualified” means here. You must have:

• 7+ years of hands‑on data engineering experience
• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field (or equivalent practical experience).
• Deep Microsoft Azure experience (required — not AWS‑only, not GCP‑only)
• Proven experience building production data pipelines that integrate complex enterprise platforms — ideally across tools like Tamarac, eMoney, Salesforce FSC, tax planning software, and AI notetaking tools (plus other vendor and internal feeds)
• Strong SQL & Python skills (Spark / PySpark experience highly valued)
• Experience with RAG, Prompt engineering, Multi-agent architectures, Memory patterns, Re-ranking, Semantic search / vector retrieval, Context augmentation, Human-in-the-loop patterns, Workflow routing or model routing, Chunking strategies for retrieval pipelines.
• Experience with Agentic Frameworks like CrewAI, Microsoft Agent Framework, LangGraph
• Real, production experience building and supporting data pipelines end‑to‑end in Microsoft Fabric
• 1.5–2+ years of applied AI / advanced analytics engineering
• Experience designing for reliability, scale, and operational ownership

Duties and Responsibilities

Design, build, and maintain scalable data pipelines integrating wealth tech platforms (e.g., Tamarac, eMoney, Salesforce FSC, Tax planning software, AI notetaking tools, etc.) into the enterprise data platform.

Develop and support ingestion patterns including APIs, SFTP/file drops, vendor extracts, streaming/near-real-time feeds, and database replication as applicable.

Model and curate conformed datasets (dimensional and/or Data Vault-style) to enable cross-platform reporting, advisor dashboards, and analytics use cases.

Implement data quality controls (validation rules, reconciliation, exception reporting), monitoring/alerting, and SLAs to ensure reliable downstream delivery.

Establish service-level agreements (SLAs) and monitoring frameworks to maintain high reliability and timely availability of enterprise data assets.

Partner with platform owners and business stakeholders to translate requirements into technical designs, estimates, and delivery plans; proactively communicate risks and dependencies.

Establish and document integration standards, naming conventions, reusable templates, and operational runbooks to improve maintainability and reduce support burden.

Implement automated testing and validation frameworks for data pipelines including unit testing, data validation checks, and regression testing.

Apply security, privacy, and governance requirements including access controls, lineage tracking, retention policies, and auditability in collaboration with compliance and information security teams.

Maintain metadata, documentation, and lineage within enterprise data governance and catalog platforms.

Support platform change management by assessing vendor release impacts, coordinating testing, and updating pipelines/models to maintain compatibility.

Optimize performance and cost across Fabric/Azure workloads through efficient transformations, partitioning strategies, and resource tuning.

Provide production support and operational ownership, including incidents. response, root cause analysis, and continuous improvement of pipeline reliability.

Collaborate with stakeholders to translate business requirements into technical solutions.

Develop reusable frameworks, documentation, and templates to institutionalize best practices.

Ensure solutions meet compliance standards and align with platform strategy.

Support the delivery of enterprise Data Cloud initiatives, including batch and near-real-time data synchronization and enabling advanced analytics capabilities.

Support integration patterns for AI Engineering use cases, including vectorized content pipelines, structured/unstructured data preparation, metadata enrichment, and retrieval-ready data foundations where applicable.

Reduce dependency on manual processes through automation, standardization, and reusable engineering patterns.

Perform other duties as required by supervisor/manager.