Role overview

Senior Data Engineer

Requirements and responsibilities

Readable role content extracted into sections for faster review.

Key Responsibilities

  • Design, develop, and maintain scalable data pipelines and ETL/ELT workflows for structured and unstructured data.
  • Build and optimize data ingestion, transformation, and data integration processes using Python and Apache Spark.
  • Develop, schedule, and monitor workflows using Apache Airflow.
  • Design and manage enterprise data warehouse solutions using Amazon Redshift.
  • Implement data models, data marts, and reporting datasets to support analytics and business intelligence requirements.
  • Optimize data processing jobs and database performance for scalability and efficiency.
  • Work with large healthcare datasets, ensuring data accuracy, consistency, and compliance with industry standards.
  • Collaborate with business stakeholders to understand reporting and data requirements.
  • Implement data quality checks, validation frameworks, and monitoring processes.
  • Troubleshoot data pipeline issues and perform root cause analysis.
  • Ensure data security, governance, and compliance requirements are adhered to.
  • Participate in code reviews, development best practices, and continuous improvement initiatives.
  • Create and maintain technical documentation, data flow diagrams, and data mapping documents.
  • Work closely with cross-functional teams including Data Science, Analytics, Product, and Engineering teams.

Mandatory Skills

  • 6+ years of experience in Data Engineering, Big Data Engineering, or related roles.
  • Mandatory experience working in the Healthcare domain.
  • Strong hands-on experience in Python development for data engineering solutions.
  • Strong hands-on experience with Apache Spark (PySpark) for large-scale data processing.
  • Hands-on experience with Apache Airflow for workflow orchestration and scheduling.
  • Strong experience designing, developing, and optimizing solutions on Amazon Redshift.
  • Advanced SQL skills with experience in query tuning and performance optimization.
  • Experience building ETL/ELT pipelines and modern data integration frameworks.
  • Strong understanding of data warehousing concepts, dimensional modelling, and data architecture.
  • Experience handling large-scale datasets and complex data transformation requirements.
  • Strong debugging, troubleshooting, and performance tuning skills.
  • Experience working with cloud-based data platforms and services (AWS or Azure) is preferred.
  • Excellent analytical and problem-solving skills.
  • Strong communication and stakeholder management capabilities.

Preferred Skills

  • Experience with AWS data services such as S3, Glue, EMR, Athena, Lambda, or Kinesis
  • Experience with Azure Data Factory, Azure Databricks, ADLS, Synapse Analytics, or other Azure data services
  • Exposure to Databricks, Snowflake, or modern Lakehouse architectures
  • Experience with CI/CD for Data Engineering pipelines
  • Knowledge of Healthcare data standards such as HL7, FHIR, EHR/EMR, Claims, Provider, Member, or Clinical Data
  • Experience with data governance, data quality, and compliance frameworks
  • Experience with Git and DevOps best practices for data engineering
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FocusData EngineerRole area
Seniority signalSeniorCandidate level
StackAWS, Azure, CI/CDPrimary skills
Location1 accepted countryEligibility

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