Job Description Sample for Data Analyst

Standard Operating Procedure: Technical Recruitment & Role Definition

Template Registry Engineering Standards (TRES)

---

1. Document Control Block

• Document ID: SOP-TR-ENG-DAT-042
• Effective Date: October 24, 2023
• Version: 2.4.0
• Review Cadence: Semi-Annual
• Owner: Julian Vance, Chief Architect

---

2. Executive Summary & Purpose

This Standard Operating Procedure (SOP) defines the institutional requirements for authoring, validating, and deploying a standardized job description for a Data Analyst within Template Registry engineering organizations. The purpose is to ensure absolute alignment between organizational data infrastructure needs, technical vetting criteria, and market positioning. Adherence to this SOP mitigates hiring bias, guarantees parity across engineering bands, and attracts high-caliber quantitative talent.

---

3. Scope & Prerequisites

• Scope: Applies to all engineering departments, hiring managers, and Talent Acquisition partners within Template Registry.
• Required Tools & Software: Workday HRIS, Greenhouse ATS, GitHub Enterprise (for engineering competency specs), Confluence (for architecture mapping).
• PPE / Environmental Controls: N/A (Digital workspace standards apply).
• Prerequisites:
  • Approved headcount requisition in Workday.
  • Completed Total Rewards market compensation benchmarking report.

---

4. Roles & Responsibilities (RACI Matrix)

---

5. Step-by-Step Procedure

Phase 1: Role Scoping & Core Competency Mapping

• Define the primary business objective of the Data Analyst role within the target engineering squad.
• Establish the technical baseline (SQL proficiency, Python/R, BI visualization tools, data warehouse architecture).
• Draft the standard Template Registry Data Analyst Job Description text utilizing the template below:

Position Title: Data Analyst (Mid/Senior)
Department: Engineering & Data Infrastructure

About Template Registry:
Template Registry builds the foundational systems powering automated template generation and distribution at scale. We process billions of telemetry and transactional events daily, requiring rigorous, mathematically sound data analysis to drive architectural and product decisions.

Position Summary:
As a Data Analyst at Template Registry, you will bridge the gap between raw telemetry and actionable business intelligence. You will design, develop, and maintain analytical models, high-performance dashboards, and predictive pipelines that inform our core infrastructure performance and reliability.

Key Responsibilities:
1. Extract, transform, and load (ETL) complex data sets from distributed storage systems (Snowflake, BigQuery) using advanced SQL and Python.
2. Design and maintain executive-facing and engineering-facing dashboards (Looker, Tableau) tracking SLOs, latency distributions, and system throughput.
3. Collaborate with Systems Architects and Software Engineers to define telemetry logging standards for microservices.
4. Execute rigorous statistical analyses, A/B tests, and cohort studies to validate infrastructure deployment efficacy.
5. Automate data quality checks and anomaly detection pipelines to ensure absolute data integrity.

Required Qualifications:
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Economics, or equivalent quantitative discipline.
- 3+ years of professional experience in a data analytics or quantitative engineering role within a high-scale SaaS or systems infrastructure environment.
- Expert-level proficiency in ANSI SQL, relational database concepts, and query performance tuning.
- Demonstrated experience with Python (Pandas, NumPy, SciPy) for data manipulation and statistical modeling.
- Hands-on experience with modern BI platforms (Looker, Tableau) and data warehouses (Snowflake, Redshift, or BigQuery).
- Strong command of version control systems (Git) and CI/CD workflows.

Preferred Qualifications:
- Familiarity with distributed stream-processing architectures (Kafka, Flink).
- Experience tracking infrastructure reliability metrics (MTTR, MTTF, latency percentiles).
- Contributions to open-source data tooling or internal platform engineering initiatives.

Phase 2: Internal Review & Validation

• Submit drafted job description to Talent Acquisition for compliance and inclusivity screening.
• Route the specification to the Chief Architect for technical threshold verification.
• Confirm salary band and remote-work eligibility with Compensation Ops.

Phase 3: Deployment & Sourcing

• Publish approved requisition to Greenhouse ATS and external boards (LinkedIn, GitHub Jobs).
• Brief the interview panel on technical assessment rubrics (SQL live-coding, system telemetry analysis case study).

---

6. Quality Assurance & Pro-Tips

Best Practices

• Quantify Impact: Ensure the job description demands proof of past business impact (e.g., "Optimized query latency by 40%") rather than merely listing tool proficiencies.
• Tool Agnosticism: While specific tools (Snowflake, Looker) are listed, emphasize foundational computer science and statistical rigor over point-solution familiarity.

Common Pitfalls to Avoid

• The "Full-Stack Data Unicorn" Trap: Do not conflate Data Analyst responsibilities with Deep Learning Engineer or Distributed Systems Architect duties. Keep the scope strictly analytical and operational.
• Vague Technical Requirements: Avoid listing "good communication skills" without pairing it with an actionable artifact (e.g., "Translate complex architectural telemetry into executive-ready metrics").

Metric Thresholds

• Time-to-Fill (TTF): Target $\le 45$ calendar days from requisition approval to offer acceptance.
• Pipeline Conversion Rate: Minimum 15% progression rate from technical take-home/live-code to onsite interview.

---

7. Frequently Asked Questions (FAQ)

• Q: How should we adjust the job description if the candidate needs heavier data engineering skills?
  • A: If the role requires building production data pipelines rather than analyzing data outputs, shift the title to "Analytics Engineer" or "Data Engineer" and re-weight Phase 1 competencies toward orchestration tools (Airflow, dbt) and distributed computing frameworks (Spark).
• Q: Are remote-work parameters mandatory in the initial drafting phase?
  • A: Yes. Template Registry policy dictates that geographic tiering and timezone constraints must be explicitly defined in Phase 2 to ensure accurate compensation banding.