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Chennai · On-site · 5-8 Yrs · Sep 29
1 to 50 Employees · Hyderābād, India
2 open roles0 posted in 7d2 in 30d
Hiring: Engineering
Job description
Overview
• Experience: 5 - 8 years • Primary Skills: GCP, BigQuery, Dataflow, Dataform, Airflow, Datastream, SQL, Python • Notice Period: Immediate • Location: Chennai
Responsibilities
- Design, build, and maintain ELT pipelines on GCP (ingestion through curated layers) using BigQuery, Dataform/Dataflow, and Cloud Composer.
- Ingest data from diverse sources — streaming (Pub/Sub), batch and file-based (Cloud Storage), and healthcare interoperability formats (FHIR/HL7 via the Healthcare API).
- Implement data-quality controls (schema validation, completeness, deduplication, referential integrity) and end-to-end data lineage.
- Apply data protection and governance — de-identification/tokenization (Cloud DLP with KMS/CMEK) and row- and column-level access controls.
- Model curated datasets (medallion / lakehouse and dimensional) for analytics, reporting, and downstream AI/ML.
- Build incremental and change-data-capture (CDC) loads and optimize for cost and performance (partitioning, clustering, query efficiency).
- Establish observability (monitoring, alerting) and CI/CD for data pipelines, and maintain metadata and catalog documentation.
- Collaborate with data architects, analysts, and stakeholders to translate requirements into reliable, well-documented data products.
Qualifications
- Primary Skills: GCP, BigQuery, Dataflow, Dataform, Airflow, Datastream, SQL, Python
- Notice Period: Immediate
- Location: Chennai
- Location: Chennai / Bangalore / Pune / Coimbatore
- Work Mode: WFO
- 4 years of data-engineering experience with hands-on Google Cloud.
- Strong SQL and Python.
- GCP data stack: BigQuery (core), plus working experience with several of — Dataflow, Dataform, Pub/Sub, Cloud Storage, Cloud Composer/Airflow, Dataplex, and Cloud IAM.
- ELT/ETL pipeline design (batch; streaming a plus) and solid data modeling (medallion / lakehouse / dimensional).
- Data-quality and pipeline-reliability practices; proficiency with Git and CI/CD for data pipelines.
- Strong communication skills and the ability to work through ambiguity.
Nice to have
- Other cloud/data stacks: AWS (Glue/Redshift/S3), Azure (Synapse/ADF), Databricks, or Snowflake.
- Transformation & IaC: dbt (in addition to Dataform); Terraform or other infrastructure-as-code.
- Healthcare/life-sciences data standards: CDISC SDTM/ADaM, FHIR/HL7, EDC/ePRO/CTMS, and real-world data/claims.
- Compliance exposure: HIPAA, 21 CFR Part 11, GxP, and de-identification/tokenization patterns.
- Security & governance: VPC Service Controls, CMEK, and Dataplex lineage/quality.
- BI & serving: Looker / LookML.
- AI/ML adjacency: Vertex AI, Feature Store, RAG/semantic layers, or AI-orchestrated (agentic) data pipelines.
Education
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience
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Last updated 6 October 2026
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