Data Analytics Engineer

Job Summary

Responsible for building data pipelines, performing analysis, and creating dashboards to deliver insights for business decisions. Requires strong SQL, Python/R, and cloud data experience

Job Roles & Responsibilities

• Pipeline Management
• 1. Design, build, and maintain ETL/ELT workflows using orchestration frameworks (e.g., Airflow, dbt).
• • Exploratory Data Analysis & Visualization
• 1. Perform EDA and statistical analysis using Python or R.
• 2. Prototype and deliver interactive charts and dashboards.
• • BI & Reporting
• 1. Develop dashboards and scheduled reports to surface KPIs and trends.
• 2. Configure real-time alerts for data anomalies or thresholds.
• • Insights Delivery & Storytelling
• 1. Translate complex analyses (A/B tests, forecasting, cohort analysis) into clear recommendations.
• 2. Present findings to both technical and non-technical audiences.
• • Collaboration & Governance
• 1. Work cross-functionally to define data requirements, ensure quality, and maintain governance.
• 2. Mentor junior team members on best practices in code, version control, and documentation.

Cultural Expectations

• You must know at least one technology from each category below:
• • Data Manipulation & Analysis
• 1. Python: pandas, NumPy
• 2. R: tidyverse (dplyr, tidyr)
• • Visualization & Dashboarding
• 1. Python: matplotlib, seaborn, Plotly
• 2. R: ggplot2, Shiny
• • BI Platforms
• 1. Commercial or Open-Source (e.g., Tableau, Power BI, Apache Superset, Grafana)
• • ETL/ELT Orchestration
• 1. Apache Airflow, dbt, or equivalent
• • Cloud Data Services
• 1. AWS (Redshift, Athena, QuickSight)
• 2. GCP (BigQuery, Data Studio)
• 3. Azure (Synapse, Data Explorer)
• • Databases & Querying
• 1. RDBMS
• 2. Strong SQL Skill (PostgreSQL, MySQL, Snowflake)
• 3. Decent Knowledge of NoSQL databases

Hiring Process

• Technical Round 1
• Technical Lead Round 2
• Managerial Round 3 (Technical) (Optional)