Auriga - Data Analytics Engineer

Job Summary :

Auriga IT is seeking a proactive, problem-solving Data Analytics Engineer / BI Engineer (SDE II) with 35 years of experience owning end-to-end data pipelines. You will partner with stakeholders across engineering, product, marketing, and finance to transform raw data into actionable insights that drive business decisions.

This role blends analytics engineering, business intelligence, and advanced data analysis, with a strong emphasis on pipeline ownership, stakeholder communication, and scalable analytics practices.

Key Responsibilities :

1. Pipeline Management :

- Design, build, and maintain robust ETL / ELT pipelines.

- Implement orchestration workflows using tools such as Airflow, dbt, or equivalent frameworks.

- Own the full lifecycle of data pipelines, including monitoring, failure handling, performance optimization, and documentation.

2. Exploratory Data Analysis & Visualization :

- Perform EDA and statistical analysis using Python or R.

- Identify trends, patterns, and anomalies in large datasets.

- Prototype and deliver interactive charts, visualizations, and exploratory dashboards.

3. BI & Reporting :

- Build and maintain dashboards and scheduled reports to surface key KPIs and business metrics.

- Enable self-service analytics for business users.

- Configure alerts and anomaly detection for key thresholds and data quality issues.

4. Insights Delivery & Storytelling :

- Translate complex analyses (A/B testing, cohort analysis, forecasting) into clear, actionable insights.

- Present findings effectively to both technical and non-technical stakeholders.

- Influence product and business decisions using data-backed recommendations.

5. Collaboration, Quality & Governance :

- Work cross-functionally with engineering, product, finance, and marketing teams to define data requirements.

- Ensure data quality, consistency, and governance standards are met.

• Clean and maintainable code
• Version control best practices
• Documentation and reproducibility

6. Large Language Models (LLMs) & Applied AI (NEW) :

- Demonstrate practical understanding of Large Language Models (LLMs) and how they can be applied within analytics and BI workflows.

• Assist with SQL generation, data exploration, and analytical reasoning.
• Summarize analytical outputs into business-ready insights and narratives.
• Support documentation, metric definitions, and stakeholder communication.

• Prompt design and context management
• Tool/function calling
• Embeddings and semantic search (high-level understanding)

- Be aware of LLM limitations including hallucinations, data privacy, bias, and cost-performance trade-offs.

- Experience integrating LLMs into internal analytics tools, dashboards, or workflows is a strong plus.

- Comfortable working with AI-powered coding editors such as Cursor, Windsurf, or similar tools.

- Use AI-assisted development responsibly for :

• Writing and refactoring Python, R, and SQL code
• Debugging data pipelines and analytics workflows
• Rapid prototyping of transformations, dashboards, and scripts

• Reviewing and validating AI-generated code
• Following Git-based version control and code review processes
• Ensuring business-critical logic remains deterministic and auditable

• Python : pandas, NumPy
• R : tidyverse (dplyr, tidyr)

• Python : matplotlib, seaborn, Plotly
• R : ggplot2, Shiny

• Commercial or open-source BI tools (Tableau, Power BI, Superset, Grafana, etc.)

• Airflow, dbt, or equivalent orchestration frameworks

• AWS (Redshift, Athena, QuickSight)
• GCP (BigQuery, Looker Studio)
• Azure (Synapse, Data Explorer)

• Strong SQL skills (PostgreSQL, MySQL, Snowflake)
• Working knowledge of NoSQL databases

- 3-5 years of experience in a data analyst, analytics engineer, or BI engineering role.

- Proven experience owning end-to-end data pipelines.

- Certifications in Power BI or Tableau are a plus.