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Career Opportunity at Team Data Management

As team data management work is more engaged on the back-line, your priority tasks are to support data pipeline reliability and quality. Being detail-oriented and accessing knowledge from multiple disciplinary is your advantage for implementing data solutions to assist the frontline.

Team data management is working on the three pillars of interface, modeling, and ecosystem as your flexibility on personal assessment and agreement with your manager.

The Data Engineer handles the data in software engineering practice and optimizes data flows.


As a team member have to self-assign a personal assessment to delegate the load between three main focuses as your preference.

  • Data Interface
    • Collaborate with IT Team and Senior Data Engineer to retrieve data from provided sources.
    • Implement retrieval pipelines (ETL) on both DB to DB and non-DB to DB.
    • Provide a data request service and maintenance of pipelines you working on.
  • Data Modeling
    • Collaborate with Team Data Innovation and Senior Data Engineer to implement data model for analytics.
    • Understanding on the data model you are working with in both technical to non-technical terms.
    • Keep the model clean and demonstrate how the model was transformed correctly.
  • Data Ecosystem
    • Create, maintain, document and control your task of data pipelines on the scheduler.
    • Learn the technology stacks, conduct analyses and recommend resolution of specific data management practice.
    • Collaborate with SRE specialized or Senior Data Engineer to backup and handle with a simple failure recovery procedures.


  • Strong interest of data-driven working with business in academic/healthcare organization.
  • Good understands to handle the data with SQL in Data Warehousing practice.
  • Ability to handle data modeling and perform Incremental ETL and CTAS concepts.
  • Familiar to implement data solutions with at least one of Python, Scala, or Java.
    • Good if you can bridge with modern language like Go or Rust.
  • Be analytics and innovates in problem identifying and solution seeker.
  • Strong mindset of growth and can-do, and willing to learn.
  • Basic understands to English read and writing.


As we didn't expect every team member to proven the skills in list but will take you an advantage if you do:

  • Bachelor's degree or equivalent experience in computer or informatics studies.
  • Familiar or Experienced with Health Information System or Health Data Science Tools (ex: ICD-10, OHDSI OMOP)
  • Experienced in working with large-scale transactional data or working in various domain of data.
    • For fresh graduated, We're happy to looking forward from your education background.
  • Experienced in our prioritized database systems of MS SQL Server, MongoDB, PostgreSQL.
  • Familiar with Version Control with Git, GitHub.
  • Familiar and Experienced in data pipeline with DAGs of Apache Airflow.
  • Certification of skills or tools from well-known vendor or hiring platform provider (ex: AWS, HackerRank)
  • Great English communication technique in both oral and writing.

Associate (Data Engineer)#

The associate level is referred from the junior to middle who can be self-organized and ship a small scale project independently.

Associate Data Engineers are always reported to Senior Data Engineer.

Data Engineer Responsibilities#

Associate Data Engineer Responsibilities are listed above, and these extend in addition:

  • Perform data cleansing along with Senior Data Engineer.

Data Engineer Requirements#

Associate Data Engineer Requirement are listed above.

Data Engineer Opportunities#

Associate Data Engineer Opportunities are listed above, and these extend in addition:

  • Familiar in perform data modeling and metadata provenance on various source using dbt.
  • Ability to handle data in dimensional model.

Senior Associate (Data Engineer)#

The senior associate level is referred to the junior and middle who successfully shipped their project and working in more specific domain of data management and leading their work with a few member.

As role Senior Associate of Data Engineer your work have assigned with one specialization to let you gain a momentum to your progress as Associate Data Engineer:

Job Competency: Specialization

Specialization is idea of career path framework to becomes increasingly expert in a specific task and set of tasks as part of personal assessment, through experience in specific area. Result in quickly grain qualification of higher position level.

Specialization is not a cross road where you won't practice on the different branch, at the higher position level you need to reach fundamental to advance practice of each specialization availables in your current level.

Analytics Engineering#

You take more focus on developing data model and ETL pipelines, checking data quality and perform query code review, act as an spokesperson of model you working with, and make an personal assessment with Team Data Innovation.

Analytics Engineering Specialized Responsibilities#

Responsibilities for the Analytics Engineering Specialized extend the Data Engineer job. In addition:

  • Working as a data specialist of specific model you are assigned, with mentorship from Senior Data Engineer or an expert (Team Data Innovation).
  • Assist the teams in create reports by manipulate data using simple to complex queries in response to business requirements.
  • Ensure metadata, data dictionaries and documentation quality that meet current and future business requirements on domain of your work.
  • Test, evaluate and provide an optimization to complex queries and legacy models.
  • Assisting a Data Engineer or teams who working or using data models.

Analytics Engineering Specialized Requirements#

Requirements for the Analytics Engineering Specialized extend the Data Engineer job. In addition:

  • Demonstrated a great leading experience data model implementation at least one practice by:
    • Taking ownership and criticize a practical use case or implementing solution with stakeholder in design review session.
    • Invented a new or redesigned practical common data model as standardization to tackle the challenge of analytical tasks.
    • Bridge between a multiple domain of data or various of source into one useful format.
    • Refactor a fragmented table of features, results in better data mart.
  • Demonstrated as a well-known person in uses of data models, sources, and practice of data standardization.
  • Familiar with defining an appropriate key for ETL in incremental load and data SLA ex. ensure data correctness.
  • Familiar with data modeling or template tools (ex: dbt, Jinja Template).
  • Good written and oral communication in both technical and non-technical.

Analytics Engineering Specialized Opportunities#

Opportunities for the Analytics Engineering Specialized extend the Data Engineer job. In addition:

  • Experienced in data transformation or standardization into well-recognized model/format of data.
  • Experienced in working with multi-dimensional data in real-time or as time-series analyses.
  • Familiar or Experienced with multiple dialect of SQL engines, especially analytics database (ex: Trino, Vertica).
  • Abilities to considering different approaches and dealing with incomplete or conflicting data.
  • Demonstrated a strong skills in Information Retrieval solution for ontological query and correctness validation.

Infrastructure Engineering#

TBA (This position is not open for Y2023Q1)

Manager (Senior Data Engineer)#

TBA (This position is not open for Y2023Q1)