Introduction to the Role:
Are you passionate about unlocking the power of data to drive innovation and transform business outcomes? Join our cutting-edge Data Engineering team and be a key player in delivering scalable, secure, and high-performing data solutions across the enterprise. As a Data Engineer, you will play a central role in designing and developing modern data pipelines and platforms that support data-driven decision-making and AI-powered products. With a focus on Python, SQL, AWS, PySpark, and Databricks, you'll enable the transformation of raw data into valuable insights by applying engineering best practices in a cloud-first environment.
We are looking for a highly motivated professional who can work across teams to build and manage robust, efficient, and secure data ecosystems that support both analytical and operational workloads.
Accountabilities:
Design, build, and optimize scalable data pipelines using PySpark, Databricks, and SQL on AWS cloud platforms.
Collaborate with data analysts, data scientists, and business users to understand data requirements and ensure reliable, high-quality data delivery.
Implement batch and streaming data ingestion frameworks from a variety of sources (structured, semi-structured, and unstructured data).
Develop reusable, parameterized ETL/ELT components and data ingestion frameworks.
Perform data transformation, cleansing, validation, and enrichment using Python and PySpark.
Build and maintain data models, data marts, and logical/physical data structures that support BI, analytics, and AI initiatives.
Apply best practices in software engineering, version control (Git), code reviews, and agile development processes.
Ensure data pipelines are well-tested, monitored, and robust with proper logging and alerting mechanisms.
Optimize performance of distributed data processing workflows and large datasets.
Leverage AWS services (such as S3, Glue, Lambda, EMR, Redshift, Athena) for data orchestration and lakehouse architecture design.
Participate in data governance practices and ensure compliance with data privacy, security, and quality standards.
Contribute to documentation of processes, workflows, metadata, and lineage using tools such as Data Catalogs or Collibra (if applicable).
Drive continuous improvement in engineering practices, tools, and automation to increase productivity and delivery quality.
Essential Skills / Experience:
4 to 6 years of professional experience in Data Engineering or a related field.
Strong programming experience with Python and experience using Python for data wrangling, pipeline automation, and scripting.
Deep expertise in writing complex and optimized SQL queries on large-scale datasets.
Solid hands-on experience with PySpark and distributed data processing frameworks.
Expertise working with Databricks for developing and orchestrating data pipelines.
Experience with AWS cloud services such as S3, Glue, EMR, Athena, Redshift, and Lambda.
Practical understanding of ETL/ELT development patterns and data modeling principles (Star/Snowflake schemas).
Experience with job orchestration tools like Airflow, Databricks Jobs, or AWS Step Functions.
Understanding of data lake, lakehouse, and data warehouse architectures.
Familiarity with DevOps and CI/CD tools for code deployment (e.g., Git, Jenkins, GitHub Actions).
Strong troubleshooting and performance optimization skills in large-scale data processing environments.
Excellent communication and collaboration skills, with the ability to work in cross-functional agile teams.
Desirable Skills / Experience:
AWS or Databricks certifications (e.g., AWS Certified Data Analytics, Databricks Data Engineer Associate/Professional).
Exposure to data observability, monitoring, and alerting frameworks (e.g., Monte Carlo, Datadog, CloudWatch).
Experience working in healthcare, life sciences, finance, or another regulated industry.
Familiarity with data governance and compliance standards (GDPR, HIPAA, etc.).
Knowledge of modern data architectures (Data Mesh, Data Fabric).
Exposure to streaming data tools like Kafka, Kinesis, or Spark Structured Streaming.
Experience with data visualization tools such as Power BI, Tableau, or QuickSight.
Work Environment & Collaboration:
We value a hybrid, collaborative environment that encourages shared learning and innovation. You will work closely with product owners, architects, analysts, and data scientists across geographies to solve real-world business problems using cutting-edge technologies and methodologies. We encourage flexibility while maintaining a strong in-office presence for better team synergy and innovation.
About Agilisium:
Agilisium is a Life Sciences Industry’s Premier Autonomous Agentic AI Services Company for “Reimagining, developing, and co-developing AI-engineered business processes that are autonomous, scalable, and built for Life Sciences.”
Agilisium delivers AI services for life sciences, helping pharma reimagine how therapies are discovered, developed, and delivered. We build domain-infused, agentic AI solutions across R&D, Clinical, and Commercial to automate workflows and enhance oversight.
Agilisium continues to invest in top-tier talent to fuel innovation, scalability, and impact across the Life Sciences value chain.
