Analytics Engineer - Job Description (JD)Job TitleAnalytics EngineerJob SummaryWe are seeking a skilled
Analytics 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 SkillsSQL & 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 ArchitectureData 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 LayerData Warehouse- Snowflake
- BigQuery
- Redshift
- Azure Synapse
Reporting LayerDeployment & Monitoring- Git
- GitHub
- CI/CD
- Docker
- Monitoring & Alerting
