Senior Data Engineer
Senior Data Engineer – Job Description
Location - Bangalore
Role Overview
We are looking for a Senior Data Engineer who provides technical leadership and hands-on ownership for complex, high-impact data engineering solutions across client engagements.
This role sits between Data Engineer and Principal Engineer, extending senior execution into cross-project design decisions, standards, and accelerators, while still delivering critical components end-to-end.
The Principal Data Engineer reduces ambiguity, drives technical direction, and raises engineering quality across teams.
Key Responsibilities
Technical Leadership & Solution Design
- Lead design for complex data subsystems (ingestion, transformation, curation, serving).
- • Drive key design choices such as batch vs streaming, lakehouse vs warehouse, orchestration approach, and partitioning strategy.
- • Define and enforce standards for pipeline design, coding practices, documentation, and production readiness.
Hands-on Data Engineering Ownership
- Design, build, and optimize ETL / ELT pipelines using Python, SQL, and distributed processing frameworks (Spark / Databricks).
- • Own complex integrations across multiple source systems (APIs, files, CDC/event streams).
- • Optimize pipelines for performance, reliability, and cost efficiency.
Orchestration, Reliability & Operations
- Architect and implement orchestration patterns (Airflow or equivalent), including retries, backfills, SLAs, and dependency management.
- • Establish monitoring, alerting, and data quality checks.
- • Lead triage and resolution of high-severity data pipeline issues.
Platform Enablement (Applied, Not Architectural-Heavy)
- Implement CI/CD practices for data pipelines.
- • Build reusable components and templates (ingestion patterns, validation utilities, standard DAGs) to accelerate delivery.
- • Partner with Architects to align solutions with modern cloud data platform patterns where needed.
Mentoring & Collaboration
- Mentor Senior and mid-level engineers through design and code reviews.
- • Clearly communicate design decisions and trade-offs to engineers, product teams, and clients/stakeholder.
Design & Architecture Articulation
- Produce and maintain design docs and Architecture Decision Records (ADRs) including trade-offs, risks, and rollout plans.
- • Communicate design intent and responsibilities across teams clearly.
Must-Have Skills
- Strong proficiency in Python and SQL.
- • Deep hands-on experience with Spark / PySpark / Databricks.
- • Solid experience designing production-grade ETL / ELT pipelines and orchestration workflows.
- • Strong understanding of data modeling, data warehouses, and data lakes.
- • Practical experience with cloud platforms (AWS / Azure / GCP).
- • Proven ability to lead technical decisions, mentor engineers, and deliver under ambiguity.
Nice-to-Have Skills
- Experience with streaming or event-driven data pipelines (Kafka, Kinesis, Pub/Sub).
- • Exposure to data observability, governance, or catalog tools.
- • Familiarity with containers, IaC, or platform enablement practices.
- • Relevant cloud or data engineering certifications.
What Success Looks Like in This Role
- You deliver the hardest and most ambiguous parts of client data platforms.
- • You reduce ambiguity by turning requirements into clear designs and execution plans.
- • Your pipelines are reliable, observable, cost-aware, and production-ready.
- • You uplift the DE practice through standards, reusable components, and mentorship.