Sr. Data Engineer / Informatica Developer

1. What s the major objective(s) of the role? Specifically, what does this person need to do to be considered a success? What will they be working on?

• Building and optimizing ETL processes using Informatica PowerCenter to extract, transform, and load data from multiple sources into centralized data warehouses or cloud environments.
• Ensuring data quality, consistency, and accuracy across systems by implementing validation, cleansing, and transformation logic.
• Developing and optimizing SQL queries for efficient data retrieval, analysis, and reporting.
• Leveraging cloud platforms (such as AWS, Azure, or Google Cloud Platform) to design scalable, secure, and cost-effective BI solutions.
• Collaborating with business stakeholders to understand reporting and analytics needs, then translating them into technical solutions.
• Enabling self-service analytics by delivering structured data models, dashboards, and reporting frameworks for end-users.

1. What are the MUST-HAVE technologies for this position?

(Please list must-have technologies / technical skills and what the candidate needs to do to be considered great at them)

• ETL / Data Integration Tools
• Informatica PowerCenter (core requirement for ETL design and data integration)
• Informatica Cloud (for hybrid/cloud data integration, if applicable)
• Databases and Query Languages
• SQL (advanced proficiency for writing complex queries, stored procedures, and performance tuning)
• Relational Databases such as Oracle, SQL Server, PostgreSQL, or MySQL
• Exposure to Data Warehousing concepts (Star/Snowflake schema, fact/dimension modeling)
• Cloud Platforms (at least one major provider)
• Azure (Synapse Analytics, Data Factory, Blob Storage)
• Data Modeling & Warehousing
• Dimensional modeling
• Data warehouse/lakehouse platforms (Snowflake, Databricks, or equivalent)

1. What are the MUST-HAVE Critical Skills for this position

(For critical skills, please also describe what the person needs to do with them to be considered very good at it.)

• Performance Optimization
• Experience in tuning ETL jobs and optimizing SQL queries for large data volumes.
• Ensuring data pipelines are efficient, reliable, and scalable.
• Data Quality & Governance
• Implementing data validation, cleansing, and transformation rules.
• Understanding of data security, compliance, and governance best practices.
• Problem-Solving & Analytical Thinking
• Strong skills in analyzing business requirements and translating them into technical solutions.
• Ability to troubleshoot complex ETL, SQL, and data pipeline issues.
• Collaboration & Communication
• Ability to work closely with business stakeholders to understand reporting needs.
• Clear communication of technical concepts to non-technical users.
• Adaptability & Continuous Learning
• Keeping up with evolving cloud technologies and BI tools.
• Flexibility to work across different databases, integration tools, and visualization platforms

1. What are the NICE TO HAVE technologies you wouldn t mind seeing on a candidate s resume?

• Advanced Cloud Data Ecosystem
• Azure: Data Factory, Databricks, Cosmos DB
• Snowflake or Databricks for modern data warehousing and lakehouse solutions
• BI & Visualization Tools
• Power BI, Tableau, Qlik, or Looker for dashboarding and self-service analytics
• Programming & Scripting Languages
• Python or R for data manipulation, automation, and advanced analytics
• Shell scripting for workflow automation and ETL orchestration
• Data Governance & Quality Tools
• Collibra, Alation, or Informatica Data Quality (IDQ) for metadata management and governance
• Master Data Management (MDM) tools for enterprise data consistency

Other Pertinent Information - Responsibilities, Skills, Qualifications, etc.

• ETL Tools: Informatica Power Center, IICS, Informatica Developer IDQ, SSIS, Metadata Manager
• Cloud & Infrastructure: AWS (Redshift, S3, Lambda, Glue, Kinesis), dbt Cloud
• Languages & Tools: Python, SQL, PySpark, DBT, Pandas
• Languages & Querying: T-SQL, Dynamic SQL, PL/SQL
• Databases: Microsoft SQL Server, Azure SQL, Oracle, PostgreSQL
• Data Warehousing: Redshift, Snowflake, Delta Lake, Apache Iceberg
• Data Engineering: Spark, Airflow, Airbyte, Stitch, Kafka
• Reporting Tools: SQL Server Reporting Services (SSRS), Power BI,
• Domains: Healthcare, Public Sector, Insurance, Retail, Finance