Data Analytics Senior Integration Data Engineer

 

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Job Description:

Data Analytics Senior Integration Data Engineer (Solventum)

3M Health Care is now Solventum

At Solventum, we enable better, smarter, safer healthcare to improve lives. As a new company with a long legacy of creating breakthrough solutions for our customers’ toughest challenges, we pioneer game-changing innovations at the intersection of health, material and data science that change patients' lives for the better while enabling healthcare professionals to perform at their best. Because people, and their wellbeing, are at the heart of every scientific advancement we pursue.

We partner closely with the brightest minds in healthcare to ensure that every solution we create melds the latest technology with compassion and empathy. Because at Solventum, we never stop solving for you.

The Impact You’ll Make in this Role

As a Senior Integration Data Engineer, you will serve as the technical backbone of our data ecosystem. You will be responsible for the full lifecycle of data integration, transforming raw, disparate healthcare data into a structured, reliable asset within our Snowflake data warehouse. You will leverage AWS Glue, FiveTran and Snowpipes to build scalable, cloud-native pipelines that empower analytics, drive clinical insights, and support our mission in the healthcare space.

.Core Responsibilities: (essential job duties and responsibilities)

• Design, develop, and test high-performance and scalable ETL processes to move data from disparate sources (including databases, APIs, and flat files) into the data warehouse.
• Implement complex data transformations and business logic within the ETL workflows to cleanse, standardize, and enrich the data according to business requirements.
• Take ownership of the end-to-end data pipeline, including monitoring, troubleshooting, and optimizing ETL jobs for performance and reliability.
• Develop and maintain comprehensive documentation for all ETL processes, including data source mapping, transformation rules, and data lineage.
• Collaborate with Data Modelers and Data Architects to understand data structures and ensure ETL processes align with physical data models.
• Implement data quality checks and validation steps within the pipelines to ensure the accuracy and integrity of the data being loaded.
• Work closely with source system owners to understand data formats and ensure seamless data extraction.
   

Required Skills and Experiences:

• Bachelor's Degree in Engineering or associated field.
• Deep, hands-on expertise with enterprise-level ETL tools such as AWS Glue, AWS S3, Informatica (IICS/PowerCenter), HVR, or Fivetran.
• Advanced proficiency in SQL, with the ability to write complex queries, stored procedures, and functions for data extraction and manipulation.
• Strong scripting skills in languages like Python, Perl, or Shell scripting to automate workflows and implement custom transformation logic.
• Proven experience building and maintaining ETL solutions in a data warehousing environment.
• Strong understanding of database fundamentals and experience working with various relational databases (e.g., Oracle, SQL Server, Postgres), NoSQL, APIs, SFTPs, etc.
• Exceptional troubleshooting and problem-solving skills, with a knack for debugging complex data pipeline issues.
• Ability to work independently and manage multiple ETL projects simultaneously.

Minimum Qualifications:

• 6+ years of dedicated experience as an ETL Developer or Engineer.
• 4+ years of intensive development experience with at least one major ETL tool (e.g., AWS Glue, Informatica, DataStage, Fivetran).
• 4+ years of experience writing complex SQL for data-intensive applications.
• Demonstrable experience performance tuning and optimizing long-running ETL jobs.
• 1+ years of experience with Snowflake, including its data loading features (e.g., Snowpipe, COPY command).

Nice-to-Haves

• Experience with cloud-native ETL/ELT services like AWS Glue, Azure Data Factory, or Google Dataflow.
• Familiarity with Change Data Capture (CDC) techniques and tools.
• Exposure to big data technologies like Spark or Hadoop.
• Basic understanding of data modeling concepts (e.g., star schema, snowflake schema).

Education:

• Bachelor’s in Computer Science, Information Systems, Engineering, science discipline, or similar

Work location: Bangalore (Hybrid)

Travel: None

   

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