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Analytics Engineer

Ova Technologies

Analytics Engineer - Job Description (JD)

Job Title

Analytics Engineer

Job Summary

We 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 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
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