Vignesh Mahalingam
Data Scientist
4+ years exp
4+ years ML exp
Python
SQL
Bash
Data Pipeline & Feature Engineering: 6/10
Active 9 days ago
Invite to interview
Download CVCV
Overview
Technical skills
Timeline
Roles
Overview
Data Engineer at a mid level specializing in end-to-end ETL and cloud-native data pipelines. The strongest proven skill is building reliable production ETL transformations and schema handling as implemented in the AWS Glue PySpark job bronze_to_silver.py which auto-detects batch folders, normalizes columns and writes partitioned Parquet. What is not evidenced is statistical analysis or modeling work, automated tests and CI, and secure secret/credential handling or orchestration DAGs in code.
Phone
Technical skills
Languages
3
Python
SQL
Bash
DevOps
8
AWS
Amazon S3
GitHub Actions
Docker
Git
CI/CD
Cortex
IAM
AI/ML
4
Airflow
dbt
Pandas
NumPy
Other
11
Snowflake
AWS Glue
pySpark
Spark
Oracle
Delta Lake
Power BI
Amazon Kinesis
GitHub
Databases
ETL/ELT
Timeline
Data Engineer
•
Middle
IQVIA
•
Full-Time
Designed a dbt-based transformation framework with layered Bronze/Silver/Gold models, including incremental patterns and automated dbt testing to improve data reliability and reduce runtime. Migrated legacy Oracle PL/SQL stored procedures into dbt on Snowflake, using incremental loading and SCD snapshots to improve performance and simplify transformation logic. Built AWS data infrastructure on S3/Glue with PySpark processing and secure cross-account access via IAM role assumption, then orchestrated end-to-end pipelines using Airflow with retries and fault routing. Optimized Snowflake compute through clustering and caching and accelerated BI reporting performance while implementing Snowpipe and CDC orchestration.
Airflow
dbt
Snowflake
Cortex
Python
SQL
Amazon S3
AWS Glue
pySpark
IAM
Git
GitHub Actions
Docker
CI/CD
AWS
Middle AI/ML Engineer
Confidence: Medium Data-centric
Data Engineer at a middle level specializing in cloud-native ETL and lakehouse ingestion pipelines. Proven strength in building reliable ETL flows using AWS Glue and PySpark as shown by the glue_jobs/bronze_to_silver.py script with explicit schemas, cleaning helpers, and partitioned parquet writes. There is little to no evidence of testing, experiment tracking, ML model work, or full production orchestration (Airflow DAG definitions or CI/CD) in the code.
Model Architecture & Training
How well models are designed and trained
Not evidenced in public code
Data Pipeline & Feature Engineering
6/10
How data is prepared for models
Clear, production-oriented data engineering work: schema definitions, ETL transforms, idempotent ingestion, S3 staging and helper scripts for synthetic data and uploads.
Evidence
ecommerce-lakehouse/glue_jobs/bronze_to_silver.py: schema definitions (StructType), data cleaning helpers (clean_date_column, rename_columns_to_snake) and parquet writes with partitionBy('batch_id')
ecommerce-lakehouse/ingestion/scripts/generate_data.py: realistic synthetic data generators for customers, products, orders with messy/clean date formats and config loading
ecommerce-lakehouse/ingestion/scripts/upload_to_s3.py: idempotent batch_id pattern, get_s3_client(), upload_file() with error handling and logging
Experimentation & Evaluation
How results are measured and tested
Not evidenced in public code
MLOps & Deployment
2/10
How models are shipped to production
Basic operational/deployment patterns for data pipelines exist (Glue Job init/commit, S3 upload flow), but no full MLOps or production orchestration code, CI/CD, monitoring or model serving.
Evidence
ecommerce-lakehouse/glue_jobs/bronze_to_silver.py: use of awsglue.job.Job init and job.commit for Glue execution
ecommerce-lakehouse/ingestion/scripts/upload_to_s3.py: logging, exit codes and boto3 client creation for uploads
Computational Efficiency
2/10
How efficiently computing resources are used
Uses Spark/Glue primitives and partitioning but lacks explicit performance tuning, repartitioning, caching, or profiling measures.
Evidence
ecommerce-lakehouse/glue_jobs/bronze_to_silver.py: writes parquet with partitionBy('batch_id') and calls DataFrame.count() for logging
ecommerce-lakehouse/glue_jobs/bronze_to_silver.py: use of pyspark.sql.functions for vectorized transforms
Research Depth & Innovation
Depth of research and new ideas
Not evidenced in public code
Expertise
E-commerce & Retail AI• Middle
Industries
Commerce• Middle
Data & Analytics• Middle
Technologies
Python• since 2022 • Middle
SQL• since 2022 • Middle
Snowflake• since 2022
Airflow• since 2022
Oracle
Delta Lake
GitHub Actions• since 2022
CI/CD• since 2022
Git• since 2022
AWS• since 2022
Docker• since 2022
pySpark• since 2022
Cortex• since 2022
GitHub
Amazon S3• since 2022
IAM• since 2022
Amazon Kinesis
GitHub• mentioned only
Recommendations
- Develop production orchestration artifacts such as Airflow DAGs with clear retry and SLA rules to demonstrate end-to-end scheduling and failure handling.
- Add automated tests and lightweight integration tests for Glue transformations and the S3 upload workflow to improve reliability and allow safer refactors.
- Include performance and scalability work: demonstrate partitioning strategy, repartition/coalesce tuning, and simple profiling notes or metrics for Spark jobs.
- Add configuration for secure credential handling and secrets (IAM roles, environment-based credential loading) and document operational runbooks.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Data Scientist
Confidence: Medium Data Engineer
Data Engineer at a mid level specializing in end-to-end ETL and cloud-native data pipelines. The strongest proven skill is building reliable production ETL transformations and schema handling as implemented in the AWS Glue PySpark job bronze_to_silver.py which auto-detects batch folders, normalizes columns and writes partitioned Parquet. What is not evidenced is statistical analysis or modeling work, automated tests and CI, and secure secret/credential handling or orchestration DAGs in code.
Statistical Rigor
Correct use of statistics
Not evidenced in public code
Data Wrangling & Cleaning
7/10
Preparing and cleaning data
Clear, production-oriented data wrangling and cleaning: multi-format date parsing, column normalization to snake_case, type casting and null filtering, plus idempotent batch handling for S3 uploads.
Evidence
ecommerce-lakehouse/glue_jobs/bronze_to_silver.py:clean_date_column, rename_columns_to_snake, price regexp_replace and null filters
ecommerce-lakehouse/ingestion/scripts/generate_data.py:messy_date and seeded random / np.random.seed usage
ecommerce-lakehouse/ingestion/scripts/upload_to_s3.py:batch_id folder pattern and upload_file error handling
Exploratory Analysis & Visualization
Exploring and visualizing data
Not evidenced in public code
Predictive Modeling
Building models that predict
Not evidenced in public code
Business Insight & Impact
2/10
Turning analysis into business value
Pipeline produces analytics-ready artifacts and a dbt fact model, but there is no documented business framing, cost-of-error reasoning or explicit KPI mapping in the code.
Evidence
ecommerce-lakehouse/ecommerce_dbt/models/marts/fact_orders.sql:fact table selection and surrogate key generation
Reproducibility & Notebook Hygiene
3/10
Clean, repeatable analysis
Basic reproducibility patterns present - seeded data generation, CLI entrypoints and structured logging - but no pinned environment, CI, tests or data-versioning tooling.
Evidence
ecommerce-lakehouse/ingestion/scripts/generate_data.py:random.seed and np.random.seed and main() entrypoint
ecommerce-lakehouse/ingestion/scripts/upload_to_s3.py:logging setup and exit codes
Expertise
Big Data• Middle
Industries
Commerce• Middle
Data & Analytics• Middle
Technologies
Databases
Spark
dbt• since 2022
Pandas
NumPy
ETL/ELT
AWS Glue• since 2022
Recommendations
- Develop production Airflow DAGs or orchestration code to explicitly implement the advertised workflow and retries/alerts.
- Hardening Glue jobs for production: add unit tests, integration tests, CI, and automated data-quality checks (schema drift, null-rate alerts).
- Add secrets management and IAM best practices for S3 and Snowflake integration instead of relying on local credentials or uncovered config files.
- Expand dbt coverage with tests and documentation and add data-versioning or DVC-like tracking for sample datasets.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Backend Developer
Confidence: Medium Data Platform
A pragmatic data engineer at a middle level who focuses on practical ETL and cloud-native data pipeline implementation. The strongest proven skill is building Glue PySpark ETL logic and data hygiene shown in glue_jobs/bronze_to_silver.py which includes explicit schemas, cleaning, and partitioned parquet writes. There is little public evidence of API services, automated tests, migration histories, retries/backoff strategies, or advanced observability and security hardening.
API Design
How well APIs are designed
Not evidenced in public code
Data Layer & Database
4/10
Working with databases
Clear schema and type handling inside Spark jobs, explicit casts and cleaning, and partitioned writes provide good data-layer hygiene but there is no migration history or database-specific tuning shown.
Evidence
ecommerce-lakehouse/glue_jobs/bronze_to_silver.py: SCHEMAS defined with StructType/StructField and used in spark.read.schema(...)
ecommerce-lakehouse/glue_jobs/bronze_to_silver.py: Price cleaning using F.regexp_replace(...).cast(DoubleType())
ecommerce-lakehouse/glue_jobs/bronze_to_silver.py: parquet writes with .partitionBy('batch_id') to create partitioned silver tables
Scalability & Performance
3/10
Handling load and speed
Uses Spark and Glue for scaled ETL and partitioned outputs with a batch id pattern for idempotency, but lacks explicit caching strategies, load-test artifacts, queue-based decoupling, or measured performance tuning.
Evidence
ecommerce-lakehouse/glue_jobs/bronze_to_silver.py: SparkContext/GlueContext usage and parquet partitioning via .partitionBy('batch_id')
ecommerce-lakehouse/ingestion/scripts/upload_to_s3.py: per-run batch_id folder pattern used to avoid overwrites and provide idempotency
System Architecture
3/10
Overall system structure
Code is organized around separate ingestion and transform stages (ingestion scripts, Glue job) reflecting a small-scale medallion-style pipeline, but there is no evidence of multi-service contracts, graceful degradation strategies, or complex decomposition trade-offs.
Evidence
ecommerce-lakehouse/glue_jobs/bronze_to_silver.py: implements Bronze-to-Silver transformation step
ecommerce-lakehouse/ingestion/scripts/upload_to_s3.py: implements ingestion/upload responsibilities separate from transformation
ecommerce-lakehouse/ingestion/scripts/generate_data.py: synthetic data generation kept separate from ingestion and transform logic
Security & Auth
2/10
Protecting data and access
Minimal security posture visible: code uses boto3 and relies on external AWS credentials but shows no explicit secrets management, token lifecycle handling, or hardened input validation.
Evidence
ecommerce-lakehouse/ingestion/scripts/upload_to_s3.py: get_s3_client returns boto3.client(...) and docstring states it uses AWS CLI credentials
ecommerce-lakehouse/glue_jobs/bronze_to_silver.py: uses boto3.list_objects_v2(...) without any explicit credential or role handling in code
Reliability & Observability
3/10
Stability and monitoring
Basic reliability and observability are present via logging, exit codes, and job.commit, and errors are caught in uploads, but advanced patterns like retries with backoff, structured tracing, metrics, timeouts, and automated testing are missing.
Evidence
ecommerce-lakehouse/ingestion/scripts/upload_to_s3.py: logging configuration, upload_file handles ClientError and FileNotFoundError and returns status; main exits with non-zero code on failures
ecommerce-lakehouse/glue_jobs/bronze_to_silver.py: prints row counts and calls job.commit() at the end
Expertise
Databases & Vector Storage• Middle
Industries
Commerce• Middle
Data & Analytics• Middle
Recommendations
- Develop production ETL pipelines and Snowflake/dbt integration end-to-end, extending the existing Glue jobs with dbt models and deployment pipelines.
- Add automated testing and CI for ingestion and transform steps including unit tests for data transforms and integration tests for S3→Glue→parquet flows.
- Introduce reliability features such as retries with exponential backoff, idempotent consumers, health checks, and structured logging/metrics for pipeline observability.
- Harden credential and secrets management (use IAM roles, environment secrets, or a secrets manager) and add input validation for external data sources
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: