About the Role
At Taglynk, we are partnering with a dynamic, fast-growing healthcare technology startup that is revolutionizing how individuals and families manage their healthcare. Health data is notoriously fragmented—scattered across hospitals, clinics, labs, and apps. This company exists to fix that by treating data as a durable, governed asset rather than a corporate byproduct.
As a Senior Data Engineer, you will not be starting from scratch—ingestion, warehousing, terminology pipelines, and analytics are already live in production. Your mission will be to deepen coverage, raise reliability, and scale the data asset as the company grows. While the core architecture is guided by the CTO and Principal Engineer, your voice will heavily shape its evolution.
Core Responsibilities
- Pipeline Development & Scale: Design, build, and operate end-to-end ETL/ELT production pipelines ingesting from product systems, EMRs, partner APIs, and event streams.
- Data Modeling & Optimization: Own schema design, partitioning, indexing, and query performance across analytical workloads. Translate clinical and business questions into performant, self-serve datasets.
- Infrastructure & Operations: Evolve the platform for throughput, freshness, cost management, and schema evolution. Instrument pipelines with robust monitoring, alerting, and data-quality checks.
- Governance & Security: Implement data classification, retention policies, access controls, and data lineage. Bake security and compliance into the fabric of the platform.
- Collaboration: Partner closely with Product, ML, and Clinical teams. Mentor peers through code reviews, technical design documents, and collaborative debugging.
Technical & Professional Requirements
Must-Have Skills & Experience
- Experience: 7–9 years of dedicated experience in Data Engineering with a proven track record of owning end-to-end production systems.
- Languages: Production-shipping proficiency in Python and SQL.
- Analytical Engines: Deep hands-on expertise with at least one columnar engine—Snowflake, GCP (BigQuery), Redshift, or ClickHouse.
- Data Transformation & Testing: Strong experience with dbt (including database testing and verification frameworks).
- Orchestration & Streaming: Production experience operating orchestration tools like Airflow (or Dagster) alongside event-driven streaming architectures like Kafka, GCP Pub/Sub, or Kinesis.
- DevOps: Hands-on experience building and maintaining CI/CD pipelines for automated data deployments.
- Engineering Mindset: A clear pattern of bringing solutions forward (design docs, migrations led). Comfortable inheriting, improving, and scaling existing systems rather than demanding a rewrite.
Preferred (Nice-to-Have)
- Experience with a GCP-native stack (BigQuery, Cloud Run, GKE, Pub/Sub).
- Background in Healthtech utilizing FHIR R4, clinical terminologies (SNOMED CT, LOINC, ICD-10), and compliance frameworks (HIPAA, SOC 2).
- Experience integrating data platforms with ML/AI workflows (feature stores, inference logging).
Why Join This Journey
This is an expert-level role for an engineer who walks in with solutions and thrives on high autonomy and low supervision. You will have a direct impact on building a trustworthy data backbone that powers real-time analytics, machine learning, and consumer-facing products.
To Apply: Please submit your resume through Taglynk highlighting your direct experience with production-scale Python, Airflow, and Cloud Data Warehouses.
