← All Roles
Data and Analytics

Senior Data Engineer, Modern Data Stack and AI Pipelines

Build the data infrastructure that makes AI actually work. Own end-to-end data pipelines, from ingestion and transformation through to feature stores and real-time serving layers. The invisible backbone of every AI initiative that GCCs can't afford to underhire for.

Apply for This Role All Open Roles

About This Role

India faces a shortage of 230,000+ data science professionals by 2026, but the acute gap is in data engineering, not data science. GCCs can hire ML engineers, but the data pipelines that feed their models are broken. This role is for a data engineer who combines modern stack proficiency (dbt, Spark, Airflow, cloud-native warehouses) with the ability to design data architectures that support both analytical and ML workloads at enterprise scale.

What You Will Do

  • Design and build scalable, production-grade data pipelines for both batch and real-time workloads using Apache Spark, Kafka, and cloud-native tools
  • Own the ELT/ETL architecture using dbt, Airflow (or Prefect/Dagster), and cloud data warehouses (Snowflake, BigQuery, Databricks Delta Lake)
  • Build and maintain feature stores for ML teams, Feast, Tecton, or custom, ensuring consistent feature availability between training and serving
  • Implement data quality frameworks, Great Expectations, Monte Carlo, or Soda, with automated monitoring and alerting
  • Design data lakehouse architectures using Delta Lake or Apache Iceberg, time travel, ACID transactions, schema evolution
  • Build real-time data streaming pipelines using Kafka, Kinesis, or Pub/Sub for low-latency ML inference serving
  • Implement data governance and lineage tracking, dbt docs, OpenLineage, Apache Atlas, or Collibra
  • Collaborate closely with ML engineers on feature engineering, training data pipelines, and model monitoring data infrastructure
  • Define and enforce data engineering best practices, testing, documentation, version control, CI/CD for data pipelines

What You Need to Succeed

  • 5+ years of data engineering experience with production pipeline ownership
  • Strong proficiency in Python and SQL for data transformation and pipeline development
  • Modern data stack expertise, dbt, Airflow/Prefect, and at least one cloud data warehouse (Snowflake, BigQuery, Databricks)
  • Apache Spark experience for large-scale data processing
  • Real-time streaming experience with Kafka or equivalent
  • Cloud data platform experience, AWS (Glue, S3, Redshift), Azure (Data Factory, ADLS, Synapse), or GCP (Dataflow, BigQuery)
  • Data quality and observability tooling experience
  • Strong understanding of data modelling, dimensional modelling, Data Vault, or medallion architecture

What Will Give You an Edge

  • Feature store implementation experience for ML pipelines
  • Delta Lake or Apache Iceberg expertise
  • Data governance and lineage tooling experience
  • Experience with LLM data pipelines, chunking, embedding, vector database ingestion
  • dbt certification or Databricks certifications

What Qfyre Offers

  • Data engineering ownership on AI-critical infrastructure, your pipelines power the models
  • Modern toolstack with no legacy constraints, dbt, Spark, cloud-native from Day 1
  • Remote-first flexibility with strong engineering peer community
  • Competitive compensation reflecting acute market scarcity for this profile

Skills and Technologies

dbtSparkKafkaSnowflakeDatabricksAirflowPythonAI Pipelines
Apply

Apply for Senior Data Engineer, Modern Data Stack and AI Pipelines

Complete the form below. Our team reviews every application personally, no automated filtering, no keyword matching. We will be in touch within two business days.

Drag and drop or browse to upload

PDF, DOC, or DOCX, max 5MB

Your application is reviewed by a domain expert, not an ATS. We do not share your details without your consent. See our Privacy Policy.

FAQ

Questions About This Role

Common questions from candidates and applicants.

What does the application process look like for the Senior Data Engineer, Modern Data Stack and AI Pipelines role?+

Submit your application via the form on this page. A Qfyre domain specialist will review your profile, not an automated keyword filter, and will be in touch within two business days if there is a strong fit. We may arrange a brief introductory call before presenting your profile to the client.

Is this Senior Data Engineer, Modern Data Stack and AI Pipelines role a permanent position or contract?+

This is a Full Time position based in Bengaluru / Remote. The working mode is Hybrid / Remote-first. Specific contract terms and benefits are discussed during the briefing process once your profile has been reviewed.

What experience level is required for the Senior Data Engineer, Modern Data Stack and AI Pipelines role?+

This role requires 5–8 years of relevant experience. The specific technical requirements and domain expectations are outlined in the full job description above. If your experience is slightly outside the stated range but you have strong relevant capability, we encourage you to apply, we assess profiles holistically, not against a checklist.

Does Qfyre assist with relocation for this role?+

Relocation support varies by client and mandate. Mention your relocation preferences in the application form and our team will clarify the client's position during the initial briefing. Most of our GCC and enterprise clients have structured relocation support programmes for senior hires.