ATS CV Template for Data Engineers — Complete Guide

Data Engineer with experience building end-to-end data pipelines using Python, SQL, Apache Spark, and orchestration in Apache Airflow. I design ELT workflows with dbt, integrating data from Kafka and batch sources into Snowflake (or BigQuery/Redshift) for analytics and reporting. In production, I focus on dataset freshness, SLA adherence, and safe schema evolution rather than “best-effort” batch jobs. For example, I delivered 99.8% SLA adherence and reduced end-to-end latency to under 5 minutes by tightening orchestration schedules, optimising incremental loads, and enforcing data quality checks in dbt.

I bring practical data-operations discipline: monitoring, incident response, and measurable reliability improvements. Using tools such as dbt tests and Airflow alerting, I track failure rates, mean time to recovery, and data completeness thresholds to prevent downstream metric drift. I also collaborate with analytics stakeholders to align definitions, validate outputs, and ensure trustworthy reporting in the warehouse. My approach balances performance and governance, including partitioning strategies, lineage-aware modelling, and repeatable release practices for production changes.

Built and operated production ingestion and transformation pipelines in Apache Airflow, combining Python jobs, Spark batch processing, and dbt transformations. Ingested ~5 TB/day from Kafka topics into a Snowflake data warehouse, using incremental models to keep processing time predictable. Maintained a 99.8% monthly SLA with end-to-end freshness under 5 minutes for priority datasets. Improved reliability by introducing automated data tests (dbt) and orchestration guards (e.g., dependency checks before downstream DAG execution), reducing pipeline failure rate by 35%.

Designed warehouse-ready data models using dimensional modelling principles and performance-aware SQL. Implemented dbt models with incremental strategies and robust handling for schema evolution, including backward-compatible changes and documented contract assumptions. Collaborated with data analysts to define metrics, validate transformations, and ensure consistent reporting across business dashboards. Managed query performance by adding clustering/partitioning strategies in Snowflake and adjusting join patterns to reduce cost and execution time for high-traffic workloads.

Created streaming and batch integration patterns, including CDC-style ingestion and event-driven transformations. Used Kafka for near-real-time movement of events, then processed them with Spark (where required) before landing clean, analytics-ready tables in the warehouse. Strengthened data quality with rule-based checks and anomaly monitoring, ensuring completeness and referential integrity before publishing datasets. Documented operational runbooks and release notes so the team could respond quickly to incidents and confidently deploy improvements.

Core engineering tools: Python for orchestration logic and transformation utilities; SQL for modelling and performance tuning; Apache Spark for distributed processing; Apache Airflow for DAG orchestration and scheduling. Transformation and quality: dbt for modular ELT, incremental models, macros, and automated data tests to protect downstream consumers. Streaming and ingestion: Apache Kafka for event delivery, plus ingestion patterns for batch files and CDC-like feeds. Warehouse technologies: Snowflake as primary, with experience adapting models and optimisation techniques for BigQuery and Amazon Redshift where applicable.

Cloud and storage: AWS services such as S3 for data landing, AWS Glue for cataloguing and ETL support, and Redshift when required by the environment. I also work with cloud-native patterns for secure data access, IAM alignment, and environment separation (dev/test/prod) to minimise deployment risk. Data governance and operations: emphasis on lineage clarity, reproducible builds, and robust monitoring so pipelines fail loudly and recover safely. I keep documentation concise but actionable, including runbooks, SLAs, and dataset definitions used by analytics teams.

Show credibility by listing relevant certifications and concrete operational practices. Examples include AWS Certified Data Engineer (or equivalent) and vendor-neutral training in data engineering fundamentals, plus hands-on experience applying those principles to production pipelines. I also track engineering health via KPIs such as SLA attainment, pipeline failure rate, data freshness, and throughput (e.g., TB/day). When possible, I reference cost-aware optimisation outcomes such as reduced warehouse query runtimes or lower compute spend for recurring workloads.

My working method is built around repeatability and safety: version control for dbt and DAG code, peer reviews for production changes, and staged deployments with rollback plans. I use test-driven thinking for data: dbt tests for constraints, completeness, and accepted ranges, plus pragmatic checks for upstream data drift. For reliability, I implement alerting thresholds and clear escalation paths, so incidents are resolved quickly with minimal impact. These practices help ensure your CV demonstrates not just technical capability, but operational maturity.