Lead/Senior Data Engineer

Job Role: Lead Data Engineer, Data & AI — Data Engineering

Location: Bengaluru, India (5 Days WFO)

Experience: 8–12 years in data engineering with atleast 2 years of leadership experience

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ROLE SUMMARY

We are hiring a Lead Data Engineer for a strongly Python-first profile, complemented by solid depth across Databricks, data modelling, T-SQL, and data architecture. You will design, build, and own production-grade Python codebases and frameworks at the core of our Databricks. Lakehouse platform, while also setting technical direction on pipeline architecture, data modelling, and platform performance, and mentoring

a team of engineers.

KEY RESPONSIBILITIES

• Design, build, and own production-grade Python codebases for the data platform — ETL/ELT pipelines, reusable internal packages,

schema validation, and testing frameworks (pytest, CI/CD).

• Write clean, well-tested, performant Python: apply OOP, concurrency (multiprocessing/asyncio), decorators, and generators to solve real

pipeline-scale problems.

• Profile and optimize slow Python pipelines end to end — from algorithmic/code-level fixes to migrating heavy workloads onto

Databricks/Spark when Python alone won't scale.

• Set and enforce Python engineering standards across the team: code reviews, packaging/versioning, structured logging, and exceptionhandling patterns.

• Design and deliver Bronze/Silver/Gold Medallion pipelines on Databricks — Delta Lake, Structured Streaming/Auto Loader, and Unity

Catalog governance.

• Own dimensional data modelling decisions — star/snowflake schema design and SCD strategy — for the platform's core data models.

• Write and review advanced T-SQL for platform workloads — window functions, MERGE/upsert logic, and execution-plan-based

performance tuning.

• Partner with Data Architects and stakeholders on ETL vs. ELT, orchestration, and cost/performance trade-offs across the platform; mentor

Data Engineers and Senior Data Engineers through design and code reviews.

MUST-HAVE QUALIFICATIONS

8–12 years in data engineering, including at least 2 years in a technical leadership or mentoring capacity. This role carries a strong Python emphasis,

complemented by hands-on depth in:

Python & Data Engineering (Primary Focus)

• Strong hands-on Python: OOP, generators, decorators, concurrency (multiprocessing/asyncio), exception handling, and unit testing

(pytest).

• Experience building production ETL/ELT pipelines, schema validation, and reusable internal Python packages/frameworks.

• Proven ability to profile, debug, and optimize Python code for performance at data-pipeline scale.

Databricks & Lakehouse

• Deep, hands-on Databricks experience: Delta Lake (ACID, time travel, OPTIMIZE/ZORDER/VACUUM), Structured Streaming, Auto Loader,

and Unity Catalog.

• Experience tuning Spark jobs at scale — data skew, cluster sizing/autoscaling, and cost optimization.

Data Modelling & Warehousing

• Strong grounding in dimensional modelling: star/snowflake schema, SCD Types 1/2/3, fact/dimension grain, and conformed dimensions.

• Working knowledge of Data Vault modelling and Kimball vs. Inmon trade-offs.

T-SQL

• Advanced T-SQL: window functions, MERGE/upsert patterns, indexing strategy, and execution-plan-based query tuning.

Data Architecture

• Proven experience designing Medallion (Bronze/Silver/Gold) architectures and evaluating ETL vs. ELT and batch vs. streaming trade-offs.

• Experience with orchestration (Databricks Workflows, Airflow, or ADF) on Azure or AWS.

GOOD TO HAVE

• PCEP / PCAP (Python Institute) or equivalent Python certification.

• Databricks Certified Data Engineer Professional (or equivalent) certification.

• Experience with data governance/compliance (GDPR/HIPAA) and cost-governance (FinOps) practices.

• Prior experience formally leading or mentoring a team of 3+ engineers.

EDUCATION

Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

SOFT SKILLS

Strong stakeholder communication; able to explain technical trade-offs to both engineering and business audiences; comfortable owning ambiguous, enterprise-scale design decisions end to end.

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