Analytics Engineer

Analytics Engineer – Job Description (JD)

Job Title

Analytics Engineer

Job Summary

We are seeking a skilledAnalytics Engineer to bridge the gap between data engineering and business analytics by building reliable, scalable, and well-modeled datasets for business intelligence and reporting. The ideal candidate should have expertise in SQL, data modeling, ETL/ELT pipelines, cloud data platforms, data warehousing, and BI tools. The Analytics Engineer will work closely with data engineers, analysts, and business stakeholders to ensure high-quality, analytics-ready data.

Key Responsibilities

• Design, build, and maintain scalable analytics data models.
• Develop and optimize ETL/ELT pipelines for data transformation.
• Build reliable data marts and semantic layers for business reporting.
• Create reusable datasets for BI tools and self-service analytics.
• Ensure data quality, consistency, and governance across platforms.
• Optimize SQL queries and improve warehouse performance.
• Collaborate with Data Engineers, Data Scientists, Product Managers, and Business Analysts.
• Automate data validation and testing.
• Document data models, transformations, and business logic.
• Monitor data pipelines and resolve production issues.
• Support dashboard development for business intelligence teams.

Required Skills

SQL & Database

• Advanced SQL
• Complex JOINs
• Window Functions
• Common Table Expressions (CTEs)
• Stored Procedures
• Views
• Query Optimization
• Data Partitioning
• Indexing

Databases:

• PostgreSQL
• MySQL
• SQL Server
• Oracle
• Snowflake
• Amazon Redshift
• Google BigQuery
• Azure SQL Database

Data Modeling

• Star Schema
• Snowflake Schema
• Fact Tables
• Dimension Tables
• Slowly Changing Dimensions (SCD)
• Data Vault (Preferred)
• Normalization
• Denormalization
• Semantic Layer Design

ETL / ELT

• dbt (Data Build Tool)
• Apache Airflow
• Azure Data Factory
• AWS Glue
• Google Cloud Dataflow
• Talend
• Informatica
• SSIS
• Python-based ETL

Programming Languages

• SQL
• Python
• Bash
• Java (Preferred)
• Scala (Optional)

Cloud Platforms

• AWS
• Microsoft Azure
• Google Cloud Platform (GCP)

Cloud Services:

• Amazon S3
• AWS Redshift
• Azure Synapse Analytics
• Azure Data Lake
• Google BigQuery
• Google Cloud Storage
• Databricks

Data Warehousing

• Snowflake
• Amazon Redshift
• Google BigQuery
• Azure Synapse Analytics
• Microsoft Fabric
• Databricks

BI & Visualization

• Power BI
• Tableau
• Looker
• LookML
• Microsoft Excel

Data Quality & Governance

• Data Validation
• Data Lineage
• Metadata Management
• Data Catalog
• Data Governance
• Data Profiling
• Data Quality Testing
• Great Expectations (Preferred)

DevOps & Version Control

• Git
• GitHub
• GitLab
• Azure DevOps
• CI/CD
• Docker
• Kubernetes (Basic)

Preferred Qualifications

• Bachelor's degree in Computer Science, Information Technology, Data Analytics, Engineering, or a related field.
• 2–6+ years of experience in analytics engineering, data engineering, or business intelligence.
• Hands-on experience with dbt and cloud data warehouses.
• Experience with Agile/Scrum methodologies.

Preferred Certifications

• dbt Fundamentals Certification
• Microsoft Certified: Fabric Analytics Engineer Associate (DP-600)
• Microsoft Certified: Power BI Data Analyst Associate (PL-300)
• Google Professional Data Engineer
• AWS Certified Data Engineer – Associate
• Snowflake SnowPro Core Certification

Soft Skills

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management.
• Attention to detail.
• Ability to translate business requirements into technical solutions.
• Collaboration and teamwork.
• Time management and prioritization.

Nice to Have Skills

• Apache Spark
• Kafka
• Delta Lake
• Machine Learning basics
• AI-powered analytics
• Data Mesh concepts
• Feature Engineering
• Predictive Analytics
• Data Observability
• MLOps fundamentals

Sample Project Responsibilities

• Build analytics-ready data models using dbt.
• Develop ELT pipelines from multiple data sources.
• Design star schema models for reporting.
• Optimize Snowflake and BigQuery warehouse performance.
• Create reusable semantic models for Power BI and Tableau.
• Implement automated data quality checks.
• Monitor data pipeline health and resolve issues.
• Document business logic and transformation rules.

Sample Analytics Architecture

Data Sources

• CRM Systems
• ERP Systems
• REST APIs
• CSV/Excel Files
• Application Databases
• Third-party Data Sources

Ingestion Layer

• Apache Airflow
• Azure Data Factory
• AWS Glue
• Kafka

Transformation Layer

• dbt
• SQL
• Python

Data Warehouse

• Snowflake
• BigQuery
• Redshift
• Azure Synapse

Reporting Layer

• Power BI
• Tableau
• Looker

Deployment & Monitoring

• Git
• GitHub
• CI/CD
• Docker
• Monitoring & Alerting