Business Intelligence Engineer

Azenta•Plainfield, IN

16h•Hybrid

About The Position

The Business Intelligence Engineer designs, develops, and maintains enterprise data and analytics solutions that support data-driven decision-making. The role builds and optimizes cloud-based data platforms, scalable ETL pipelines, and curated datasets for reporting, dashboards, advanced analytics, and AI initiatives. Working across AWS, Snowflake, and PostgreSQL environments, the engineer turns business and operational data into trusted, actionable insights while supporting data quality, governance, and scalability. The position partners with business stakeholders to understand analytics requirements and collaborates with Data Science and AI teams on machine-learning data preparation. It also supports self-service analytics, enterprise reporting, and governed data assets. The work is performed in a general office setting under a hybrid schedule of four days in the office and one day remote.

Requirements

Bachelor's degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related field.
At least 4 years of experience in Business Intelligence, Data Engineering, Analytics Engineering, or related disciplines.
Strong experience with ETL development and data integration.
Hands-on experience with Snowflake.
Hands-on experience with PostgreSQL, including query optimization, schema design, indexing strategies, and performance tuning.
Strong SQL development, data modeling, and PostgreSQL database development expertise.
Experience working in AWS cloud environments.
Experience developing and managing enterprise reporting datasets.
Knowledge of dimensional modeling, data warehousing, and analytics best practices.
Experience with Python or another scripting language for data processing.

Nice To Haves

Experience with Amazon SageMaker and machine-learning data preparation.
Experience with AWS Glue, Athena, Lambda, and S3-based data lake architectures.
Experience with Power BI, Tableau, or similar analytics platforms.
Familiarity with Apache Iceberg, Parquet, or modern data lake table formats.
AWS and/or Snowflake certifications.
Experience supporting AI, predictive analytics, or enterprise data strategy initiatives.
Experience with PostgreSQL advanced features such as partitioning, materialized views, JSON/JSONB, extensions, and analytical query optimization.
Experience implementing medallion architecture (Bronze/Silver/Gold) or similar multi-layer data lake design patterns.
Experience with Change Data Capture (CDC), event-driven architectures, data products, and operational analytics platforms.

Responsibilities

Design, develop, and maintain ETL pipelines integrating data from enterprise applications, operational systems, APIs, and third-party platforms.
Build automated processes to extract, load, and transform structured and unstructured data into analytics-ready datasets.
Implement data validation, cleansing, enrichment, and monitoring processes.
Optimize data pipelines for performance, scalability, and reliability.
Design and manage Snowflake databases, schemas, tables, views, and data models.
Develop and maintain dimensional, operational, and semantic data models for analytics and reporting.
Create structured, governed datasets that enable self-service business intelligence.
Optimize Snowflake performance through clustering, partitioning, and query tuning.
Develop and support AWS-based analytics solutions using services including S3, SageMaker, Glue, Athena, Lambda, and Aurora PostgreSQL.
Design and maintain S3 data lake structures and table architectures for enterprise reporting and analytics.
Support ingestion and transformation of data from multiple internal and external sources.
Design, develop, and optimize PostgreSQL database objects and data transformation logic for analytics and reporting workloads.
Partner with business stakeholders to understand reporting and analytics requirements.
Create and maintain reusable datasets, KPIs, metrics, and reporting structures.
Support dashboard and visualization development using BI and reporting tools.
Enable self-service analytics through governed data models and semantic layers.
Collaborate with Data Science and AI teams to prepare and curate datasets for machine-learning models.
Support data engineering activities for Amazon SageMaker and AI initiatives.
Develop scalable data assets for predictive analytics and operational intelligence.
Establish and maintain data quality standards and controls.
Document data lineage, mappings, business rules, and transformation logic.
Ensure compliance with data governance, security, and regulatory requirements.
Participate in master data and metadata management initiatives.