Analytics Engineer

Embedded in a lean data team at a privately held $11 billion bank, the Analytics Engineer turns disparate data into a single source of truth that drives revenue growth, expense discipline, and risk management. Partnering with data engineers, you architect reusable models that align deposits, loans, and operational metrics to executive KPIs. Rigorous testing, documentation, and peer review make these assets dependable enough for regulators yet agile enough for daily forecasting. Workshops and clear guides empower business, operations, and financial analysts to run their own analyses without IT intervention. Ultimately, you transform raw data into confident decisions that protect the bank and propel its strategy.

Essential Functions: 

• Model & Transform – Translate user stories into data‑model designs, then write SQL / Python / dbt transformations that turn raw core‑banking, lending, and operational feeds into governed, reusable assets (Fabric semantic models, data marts, SQL views, AS tabular models).
• Engineer with Rigor – Apply software‑engineering best practices—Git branching, pull‑request code reviews, automated unit/integration tests, and CI/CD pipelines—to guarantee every dataset is versioned, auditable, and production‑
• Partner & Plan – Work shoulder‑to‑shoulder with Data Engineers on ingestion architecture and with business stakeholders to refine acceptance criteria, ensuring models answer the right questions at the right granularity.
• Monitor & Remediate – Implement data‑quality tests and real‑time alerts so freshness, accuracy, and uptime meet expectations; troubleshoot incidents end‑to‑
• Enable Self‑Service – Coach financial, business, and operations analysts on BI tools and storytelling best practices; create workshops, playbooks, and template dashboards that raise data literacy across the bank.
• Govern & Document – Contribute to business glossaries, data dictionaries, lineage diagrams, and Purview catalogs so every field is traceable from source to report and compliant with FFIEC/FDIC expectations.
• Continuously Improve – Lead retrospectives to refine delivery methodology, evaluate emerging tooling (e.g., Fabric Copilot, dbt‑Mesh, Data Contracts), and pursue certifications or training that keep the team ahead of industry trends.

Situational Functions:

• Perform ad‑hoc/statistical analyses and present insights to decision‑
• Design lightweight workflow automations (Power Automate / Power Apps) where data pipelines intersect business processes.
• Estimate solution cost, effort, and complexity for new initiatives.
• Enforce and help evolve IT standards, controls, and Secure SDLC procedures.
• Participate in special projects, compliance reviews, and risk assessments as assigned.
• Stay current through conferences, research, and peer learning; share findings with the team.
• Maintain regular, reliable attendance.

Required Skills: 

• Education & Experience – Bachelor’s in Data Analytics, MIS, Computer Science, or related field or equivalent professional experience; 3 + years delivering production data models or pipelines.
• SQL & Data Modeling – Expert at writing performant SQL (T‑SQL or ANSI), dimensional modeling, and building semantic layers (Fabric, SSAS Tabular, or dbt).
• Version Control & CI/CD – Daily Git workflow (branching, PR review) plus automated testing and deployment via Azure DevOps, GitHub Actions, or similar.
• BI & Visualization – Strong Power BI skills (DAX, model optimization, role‑based security); ability to translate business questions into dashboards and reports.
• Quality & Monitoring – Experience with data‑quality tests, lineage tools, and alerting to maintain high‑uptime, audit‑ready pipelines.
• Cloud & Orchestration – Exposure to Azure Fabric, Synapse, Databricks, ADF, or comparable cloud data platforms.
• Soft Skills – Proven ability to prioritize multiple initiatives, communicate with technical and non‑technical stakeholders, and thrive under tight deadlines in a regulated environment.

Preferred Skills:

• Banking / FinTech Domain – Familiarity with data structures, payment, credit and other functions relating to financial services.
• Programming & Scripting – Proficiency in Python for data transformation, orchestration, or testing.
• Workflow Automation – Power Automate, Power Apps, or other low‑code tools to streamline business processes.
• Programming & APIs – REST API integration, ba