Data Engineering Job Support (Python)
Modern data engineering means building reliable, scalable pipelines that ingest, transform, and serve data to analytical consumers — all in Python. When your Airflow DAG silently fails, your Spark job hits shuffle memory limits, or your dbt model produces wrong results, our data engineering job support gets you back on track fast.
Apache Airflow
- DAG design – task dependencies, XComs, branching with BranchPythonOperator
- Operators – PythonOperator, BashOperator, custom operators, Sensors
- Scheduling – cron expressions, data-aware scheduling, backfill
- Connections & hooks – database, S3, Snowflake, BigQuery hooks
- Monitoring – SLAs, alerts, Flower for Celery workers
- Managed Airflow – MWAA (AWS), Cloud Composer (GCP), Astronomer
PySpark & Databricks
- DataFrame API – transformations, actions, caching, partitioning
- SparkSQL – window functions, lateral views, CTE
- Performance tuning – shuffle partition size, broadcast joins, adaptive query execution
- Delta Lake – MERGE operations, schema evolution, VACUUM / OPTIMIZE
- Structured Streaming – trigger modes, watermarks, stateful processing
- Cluster configuration – auto-scaling, spot instances, init scripts
dbt (data build tool)
- Model organisation – staging, intermediate, mart layers
- Jinja templating – macros, packages, dynamic SQL
- Incremental models – unique key, merge strategy
- Tests – generic and singular, dbt-expectations
- Source freshness, documentation, lineage graph
Python ETL & Data Processing
- Pandas for medium-scale transformations
- Polars – lazy vs. eager evaluation, schema inference
- Great Expectations – data validation, expectation suites, data docs
- Custom connectors – reading from APIs, FTP, legacy databases
Data Warehouse Integration
- Snowflake – Python connector, Snowpark DataFrames, COPY INTO
- BigQuery – google-cloud-bigquery client, load jobs, streaming inserts
- Redshift – psycopg2, SQLAlchemy, COPY from S3