Senior Data Engineer

About Us:

Senior Data Engineer

Company Website- https://fluentinhealth.com/

Senior Data Engineer

Youll work closely with engineering, product, analytics, and compliance teams to establish data best practices, ensure data governance, and unlock insights for internal teams and future data monetization initiatives.

Responsibilities:

Architecture & Strategy:

• Own and evolve the

  target data architecture

  , with a focus on

  ClickHouse

  for large-scale analytical and real-time querying workloads.
• Define and maintain a

  scalable and secure data platform architecture

  that supports various use cases including real-time analytics, reporting, and ML applications.
• Set

  data governance and modeling standards

  , and ensure data lineage, integrity, and security practices are followed.
• Evaluate and integrate complementary technologies into the data stack (e.g., message queues, data lakes, orchestration frameworks).

Data Engineering:

• Design, develop, and maintain robust

  ETL/ELT pipelines

  to ingest and transform data from diverse sources into our data warehouse.
• Optimize

  ClickHouse schema and query performance

  for real-time and historical analytics workloads.
• Build data APIs and interfaces for product and analytics teams to interact with the data platform.
• Implement monitoring and observability tools to ensure pipeline reliability and data quality.

Collaboration & Leadership:

• Collaborate with data consumers (e.g., product managers, data analysts, ML engineers) to understand data needs and translate them into scalable solutions.
• Work with security and compliance teams to implement

  data privacy, classification, retention, and access control policies

  .
• Mentor junior data engineers and contribute to hiring efforts as we scale the team.

Qualifications:

• 5-7 years

  of experience in

  Data Engineering

  , with at least

  2-4 years

  in a

  Senior or Architectural

  role.

• Expert-level proficiency

  in

  ClickHouse

  or similar columnar databases (e.g., BigQuery, Druid, Redshift).
• Proven experience designing and operating scalable

  data warehouse and data lake architectures

  .
• Deep understanding of

  data modeling

  ,

  partitioning

  ,

  indexing

  , and

  query optimization

  techniques.
• Strong experience building

  ETL/ELT pipelines

  using tools like Airflow, dbt, or custom frameworks.
• Familiarity with

  stream processing

  and

  event-driven architecture

  (e.g., Kafka, Pub/Sub).
• Proficiency with

  SQL

  and at least one programming language like

  Python

  ,

  Scala

  , or

  Java

  .
• Experience with

  data governance

  ,

  compliance frameworks

  (e.g., HIPAA, GDPR), and

  data cataloging

  tools.
• Knowledge of

  real-time analytics

  use cases and streaming architectures.
• Familiarity with

  machine learning pipelines

  and integrating data platforms with ML workflows.
• Experience working in regulated or high-security domains like

  Healthtech

  ,

  Fintech

  , or

  Enterprise SaaS.