Data Analytics Engineer
Summary
Builds and maintains scalable data pipelines, analyzes data, and develops AI-driven predictive models to support business decisions using SQL, BI tools, and ML techniques.
Roles & Responsibilities
Data Engineer/Analyst to design, build, and maintain scalable data pipelines and deliver actionable analytics, including AI-driven analytics and predictive/forecasting models, to support business decision-making. This role bridges data infrastructure engineering with analytical and predictive reporting, build robust ETL/ELT pipelines, apply machine learning for forecasting, and translate data into insights via dashboards and ad hoc analysis.
Key Responsibilities: Design, build, and maintain ETL/ELT pipelines to ingest, transform, and load data from multiple sources (databases, APIs, files, streaming) Develop and optimize data models for analytics and reporting Build and maintain dashboards/reports using BI tools Write efficient SQL queries and perform data analysis to answer business questions Ensure data quality, integrity, and governance across pipelines Monitor and troubleshoot data pipeline performance and failures Collaborate with stakeholders to gather requirements and translate them into data solutions Document data flows, schemas, and definitions for maintainability Support ad hoc data requests and exploratory analysis Develop AI-driven analytics and predictive models (forecasting, trend detection, anomaly detection) to support data-driven decision-making Apply machine learning techniques to historical data for forecasting business/operational outcomes (e.g., demand, cost, schedule risk) Tell employers what skills you have Design Database Schemas Pipeline Management Pipelines Data Pipeline Database Systems Transformation requirements from stakeholders Data Quality Assurance Data Integrity Design-Build Business Intelligence (Bi) Tools Streaming Media API SQL Development Reporting & Analysis
Data Engineer/Analyst to design, build, and maintain scalable data pipelines and deliver actionable analytics, including AI-driven analytics and predictive/forecasting models, to support business decision-making. This role bridges data infrastructure engineering with analytical and predictive reporting, build robust ETL/ELT pipelines, apply machine learning for forecasting, and translate data into insights via dashboards and ad hoc analysis.
Key Responsibilities: Design, build, and maintain ETL/ELT pipelines to ingest, transform, and load data from multiple sources (databases, APIs, files, streaming) Develop and optimize data models for analytics and reporting Build and maintain dashboards/reports using BI tools Write efficient SQL queries and perform data analysis to answer business questions Ensure data quality, integrity, and governance across pipelines Monitor and troubleshoot data pipeline performance and failures Collaborate with stakeholders to gather requirements and translate them into data solutions Document data flows, schemas, and definitions for maintainability Support ad hoc data requests and exploratory analysis Develop AI-driven analytics and predictive models (forecasting, trend detection, anomaly detection) to support data-driven decision-making Apply machine learning techniques to historical data for forecasting business/operational outcomes (e.g., demand, cost, schedule risk) Tell employers what skills you have Design Database Schemas Pipeline Management Pipelines Data Pipeline Database Systems Transformation requirements from stakeholders Data Quality Assurance Data Integrity Design-Build Business Intelligence (Bi) Tools Streaming Media API SQL Development Reporting & Analysis