Lead Software Engineer

Caterpillar Caterpillar · Industrial · Chennai, Tamil Nadu

Lead Software Engineer with deep DBA and SQL expertise to evolve a data platform powering analytics, personalization, and campaign measurement. Responsibilities include designing/optimizing databases, implementing data models, ensuring security/compliance, and mentoring engineers.

What you'd actually do

  1. Design, implement, and maintain high‑availability OLTP/OLAP databases; plan capacity, perform upgrades/patching, automate backups & disaster recovery (RPO/RTO ownership).
  2. Build and optimize complex SQL (CTEs, window functions, materialized views) for ETL/ELT, reporting, and application services; champion query performance and cost efficiency.
  3. Define and maintain conceptual, logical, and physical data models (3NF/BCNF, star/snowflake schemas, SCDs); establish and enforce data modeling standards and naming conventions.
  4. Diagnose and resolve bottlenecks (indexing, partitioning, statistics, plan regression); implement observability (query performance baselines, slow query analysis, I/O/latency dashboards).
  5. Implement RBAC/ABAC, data masking, encryption at rest/in transit, auditing, and compliance guardrails; drive data quality SLAs and lineage.

Skills

Required

  • 10+ years in software engineering or data engineering roles
  • 5+ years focused on databases as a DBA/Database Engineer/Lead
  • Expert SQL skills
  • performance tuning on at least one major platform (e.g., PostgreSQL, MySQL, SQL Server, Oracle, Snowflake, Redshift, BigQuery)
  • DBA depth: Indexing/partitioning strategies, vacuum/analyze/auto‑stats, query plans, replication, backup/restore, PITR, high availability (e.g., replication, clustering), and DR planning
  • Data modeling mastery: Strong command of normalization/denormalization tradeoffs, dimensional modeling (Kimball/Inmon), SCD types, surrogate keys, and evolution strategies
  • Data quality & governance: Experience with validation frameworks, constraints, lineage, and documentation; comfortable defining SLAs/SLOs for data
  • Security/compliance: Practical experience implementing least privilege, encryption, auditing, and compliance-aware design
  • Leadership: Proven experience mentoring engineers and leading technical initiatives end‑to‑end

Nice to have

  • Python for data tooling, ETL/ELT jobs, data validation, and automation (e.g., Pandas, SQLAlchemy, dbt adapters, Airflow operators)
  • AWS (e.g., RDS/Aurora, Redshift, S3, Glue, Lambda, EMR, IAM, KMS, Secrets Manager) and infrastructure-as-code (e.g., Terraform/CloudFormation)
  • Marketing/MarTech domain: Familiarity with campaign orchestration, CDP/DMP, attribution/measurement, clickstream, consent/PII handling, and omni‑channel data integration (ad platforms, email/SMS, web/app analytics)
  • Experience with streaming and CDC (Kafka/Kinesis, Debezium), dbt, Airflow, Great Expectations, Looker/Power BI/Tableau, or feature stores
  • Exposure to privacy and data regulations (GDPR/CCPA/LGPD) and consent management

What the JD emphasized

  • exceptional SQL expertise
  • deep DBA experience
  • mission-critical databases
  • high-impact engineering initiatives
  • reliable, high‑quality data capabilities
  • high‑availability OLTP/OLAP databases
  • RPO/RTO ownership
  • complex SQL
  • query performance and cost efficiency
  • conceptual, logical, and physical data models
  • data modeling standards
  • bottlenecks
  • observability
  • RBAC/ABAC
  • data masking
  • encryption at rest/in transit
  • auditing
  • compliance guardrails
  • data quality SLAs
  • technical roadmaps
  • robust data solutions
  • CI/CD for database changes
  • schema validation
  • environment parity
  • root‑cause analysis and remediation for data incidents
  • preventive controls
  • Expert SQL skills
  • performance tuning
  • DBA depth
  • Indexing/partitioning strategies
  • vacuum/analyze/auto‑stats
  • query plans
  • replication
  • backup/restore
  • PITR
  • high availability
  • DR planning
  • Data modeling mastery
  • normalization/denormalization tradeoffs
  • dimensional modeling
  • SCD types
  • surrogate keys
  • evolution strategies
  • Data quality & governance
  • validation frameworks
  • constraints
  • lineage
  • documentation
  • SLAs/SLOs for data
  • Security/compliance
  • least privilege
  • encryption
  • auditing
  • compliance-aware design
  • Leadership
  • mentoring engineers
  • leading technical initiatives end‑to‑end
  • Python for data tooling
  • ETL/ELT jobs
  • data validation
  • automation
  • AWS
  • infrastructure-as-code
  • Marketing/MarTech domain
  • campaign orchestration
  • CDP/DMP
  • attribution/measurement
  • clickstream
  • consent/PII handling
  • omni‑channel data integration
  • streaming and CDC
  • dbt
  • Airflow
  • Great Expectations
  • feature stores
  • privacy and data regulations
  • consent management