189. Senior Data Engineer
Senior Data Engineer building and operating an AWS-native data platform for healthcare claims and tokenized identifiers: writing Spark (PySpark/Scala) ETL pipelines over Parquet on S3, querying with Athena, orchestrating with Airflow, and tuning performance and cost, with strong emphasis on privacy and secure data handling.
- Compensation
- Not disclosed
- Location
- Medellín, Antioquia, Colombia; Quito, Pichincha, Ecuador
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Contract
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 5+ years of professional experience in Data Engineering
- Strong Apache Spark experience (PySpark or Scala): joins, intersections, partitioning, performance tuning
- Strong hands-on experience with the AWS data stack (S3, Athena)
- Amazon S3: Parquet datasets, partition strategies, data layout best practices
- Amazon Athena: SQL, query optimization, managing large datasets
- Experience building and operating pipelines using Airflow (DAGs, scheduling, dependencies, backfills)
- Excellent SQL skills and solid data modeling fundamentals
- Advanced English: lead technical discussions, write documentation, work with US-based stakeholders
- Proven client-facing experience
- Excellent communication and expectation management skills
Nice to have
- Familiarity with AWS-native data lake patterns (Glue Catalog, Lake Formation)
- Experience with dbt (core, tests, documentation, exposures)
- Familiarity with healthcare data (claims, eligibility, member-level datasets)
- Experience with tokenization, identity resolution, or privacy-preserving data workflows
- Knowledge of AWS security concepts: IAM, KMS, encryption, secure data handling
- Experience running Spark on AWS (e.g. EMR) or Spark-on-containers architectures
Full description
We’re looking for a Senior Data Engineer to join Source Meridian.
About Source Meridian
Source Meridian is a development software company that works to solve the industry’s most challenging problems in healthcare practices. We are laser focused on specific technologies in the healthcare and life science industries: Healthcare technology, artificial intelligence, and healthcare interoperability.
About the Role
We're looking for a Senior Data Engineer to help build and operate an AWS-native data platform processing healthcare claims data and tokenized identifiers. You'll design and implement Spark-based pipelines that transform, intersect, and enrich tokenized datasets stored primarily as Parquet on S3, queried via Athena and related AWS services. This environment intentionally avoids managed lakehouse platforms (e.g., no Databricks and no Snowflake)—you'll be doing "real" data engineering directly on AWS.
What You’ll Do
-
Build and maintain Spark pipelines to process large-scale Parquet datasets on S3.
-
Implement tokenization workflows, including transit token → real token conversion and dataset intersection/join logic.
-
Process and deliver healthcare claims datasets for matched individuals, ensuring accurate identity mapping and data integrity.
-
Orchestrate data pipelines using Airflow and/or AWS-native orchestration tools when appropriate.
-
Develop reliable, testable, and observable ETL/ELT processes (retries, idempotency, monitoring, reprocessing).
-
Optimize performance and cost across Spark jobs, S3 partitioning/layout, and Athena query patterns.
-
Contribute to dbt models when applicable (transformations, documentation, data quality checks).
-
Collaborate with cross-functional stakeholders in a healthcare environment, with a strong focus on privacy and secure data handling.
Required Qualifications
-
5+ years of professional experience in Data Engineering.
-
Strong experience with Apache Spark (PySpark or Scala), including joins, intersections, partitioning, and performance tuning.
-
Strong hands-on experience with the AWS data stack, including:
-
Amazon S3 (Parquet datasets, partition strategies, data layout best practices)
-
Amazon Athena (SQL, query optimization, managing large datasets)