Business Intelligence Engineer

Azenta Life Sciences•Plainfield, IN

1d•$101,000 - $126,000•Hybrid

About The Position

The Business Intelligence Engineer designs, develops, and maintains enterprise data and analytics solutions that enable data-driven decision-making across the organization. The role builds and optimizes cloud-based data platforms, creates scalable ETL pipelines, and develops curated datasets for reporting, dashboards, advanced analytics, and artificial intelligence initiatives. Working with AWS, Snowflake, and PostgreSQL environments, the engineer transforms complex business and operational data into trusted insights while supporting data quality, governance, and scalability. The position partners with business stakeholders to understand analytics requirements and works with Data Science and AI teams to prepare datasets for machine learning and predictive analytics. It also establishes data quality controls, documents lineage and transformation logic, and supports governance, security, regulatory compliance, and master data and metadata initiatives. The role is based in a general office setting and follows a hybrid schedule of four days in the office and one day working remotely.

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 other scripting languages 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.
Automate extraction, loading, and transformation of structured and unstructured data into analytics-ready datasets.
Implement data validation, cleansing, enrichment, and monitoring processes, and optimize 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 and governed datasets for analytics, reporting, and self-service business intelligence.
Optimize Snowflake performance through clustering, partitioning, and query tuning.
Develop and support AWS-based analytics solutions using Amazon S3, Amazon SageMaker, AWS Glue, Amazon Athena, AWS Lambda, and Amazon Aurora PostgreSQL.
Design and maintain S3 data lake structures and table architectures for enterprise reporting and analytics.
Support data ingestion and transformation from 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, and develop scalable 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.