claude-skills-reference/engineering-team/senior-data-engineer/SKILL.md

name, description

name

description

senior-data-engineer

Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.

Senior Data Engineer

Production-grade data engineering skill for building scalable, reliable data systems.

Table of Contents

1. Trigger Phrases
2. Quick Start
3. Workflows
   • Building a Batch ETL Pipeline
   • Implementing Real-Time Streaming
   • Data Quality Framework Setup
4. Architecture Decision Framework
5. Tech Stack
6. Reference Documentation
7. Troubleshooting

---

Trigger Phrases

Activate this skill when you see:

Pipeline Design:

• "Design a data pipeline for..."
• "Build an ETL/ELT process..."
• "How should I ingest data from..."
• "Set up data extraction from..."

Architecture:

• "Should I use batch or streaming?"
• "Lambda vs Kappa architecture"
• "How to handle late-arriving data"
• "Design a data lakehouse"

Data Modeling:

• "Create a dimensional model..."
• "Star schema vs snowflake"
• "Implement slowly changing dimensions"
• "Design a data vault"

Data Quality:

• "Add data validation to..."
• "Set up data quality checks"
• "Monitor data freshness"
• "Implement data contracts"

Performance:

• "Optimize this Spark job"
• "Query is running slow"
• "Reduce pipeline execution time"
• "Tune Airflow DAG"

---

Quick Start

Core Tools

# Generate pipeline orchestration config
python scripts/pipeline_orchestrator.py generate \
  --type airflow \
  --source postgres \
  --destination snowflake \
  --schedule "0 5 * * *"

# Validate data quality
python scripts/data_quality_validator.py validate \
  --input data/sales.parquet \
  --schema schemas/sales.json \
  --checks freshness,completeness,uniqueness

# Optimize ETL performance
python scripts/etl_performance_optimizer.py analyze \
  --query queries/daily_aggregation.sql \
  --engine spark \
  --recommend

---

Workflows

Workflow 1: Building a Batch ETL Pipeline

Scenario: Extract data from PostgreSQL, transform with dbt, load to Snowflake.

Step 1: Define Source Schema

-- Document source tables
SELECT
    table_name,
    column_name,
    data_type,
    is_nullable
FROM information_schema.columns
WHERE table_schema = 'source_schema'
ORDER BY table_name, ordinal_position;

Step 2: Generate Extraction Config

python scripts/pipeline_orchestrator.py generate \
  --type airflow \
  --source postgres \
  --tables orders,customers,products \
  --mode incremental \
  --watermark updated_at \
  --output dags/extract_source.py

Step 3: Create dbt Models

-- models/staging/stg_orders.sql
WITH source AS (
    SELECT * FROM {{ source('postgres', 'orders') }}
),

renamed AS (
    SELECT
        order_id,
        customer_id,
        order_date,
        total_amount,
        status,
        _extracted_at
    FROM source
    WHERE order_date >= DATEADD(day, -3, CURRENT_DATE)
)

SELECT * FROM renamed

-- models/marts/fct_orders.sql
{{
    config(
        materialized='incremental',
        unique_key='order_id',
        cluster_by=['order_date']
    )
}}

SELECT
    o.order_id,
    o.customer_id,
    c.customer_segment,
    o.order_date,
    o.total_amount,
    o.status
FROM {{ ref('stg_orders') }} o
LEFT JOIN {{ ref('dim_customers') }} c
    ON o.customer_id = c.customer_id

{% if is_incremental() %}
WHERE o._extracted_at > (SELECT MAX(_extracted_at) FROM {{ this }})
{% endif %}

Step 4: Configure Data Quality Tests

# models/marts/schema.yml
version: 2

models:
  - name: fct_orders
    description: "Order fact table"
    columns:
      - name: order_id
        tests:
          - unique
          - not_null
      - name: total_amount
        tests:
          - not_null
          - dbt_utils.accepted_range:
              min_value: 0
              max_value: 1000000
      - name: order_date
        tests:
          - not_null
          - dbt_utils.recency:
              datepart: day
              field: order_date
              interval: 1

Step 5: Create Airflow DAG

# dags/daily_etl.py
from airflow import DAG
from airflow.providers.postgres.operators.postgres import PostgresOperator
from airflow.operators.bash import BashOperator
from airflow.utils.dates import days_ago
from datetime import timedelta

default_args = {
    'owner': 'data-team',
    'depends_on_past': False,
    'email_on_failure': True,
    'email': ['data-alerts@company.com'],
    'retries': 2,
    'retry_delay': timedelta(minutes=5),
}

with DAG(
    'daily_etl_pipeline',
    default_args=default_args,
    description='Daily ETL from PostgreSQL to Snowflake',
    schedule_interval='0 5 * * *',
    start_date=days_ago(1),
    catchup=False,
    tags=['etl', 'daily'],
) as dag:

    extract = BashOperator(
        task_id='extract_source_data',
        bash_command='python /opt/airflow/scripts/extract.py --date {{ ds }}',
    )

    transform = BashOperator(
        task_id='run_dbt_models',
        bash_command='cd /opt/airflow/dbt && dbt run --select marts.*',
    )

    test = BashOperator(
        task_id='run_dbt_tests',
        bash_command='cd /opt/airflow/dbt && dbt test --select marts.*',
    )

    notify = BashOperator(
        task_id='send_notification',
        bash_command='python /opt/airflow/scripts/notify.py --status success',
        trigger_rule='all_success',
    )

    extract >> transform >> test >> notify

Step 6: Validate Pipeline

# Test locally
dbt run --select stg_orders fct_orders
dbt test --select fct_orders

# Validate data quality
python scripts/data_quality_validator.py validate \
  --table fct_orders \
  --checks all \
  --output reports/quality_report.json

---

Workflow 2: Implementing Real-Time Streaming

Scenario: Stream events from Kafka, process with Flink/Spark Streaming, sink to data lake.

Step 1: Define Event Schema

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "UserEvent",
  "type": "object",
  "required": ["event_id", "user_id", "event_type", "timestamp"],
  "properties": {
    "event_id": {"type": "string", "format": "uuid"},
    "user_id": {"type": "string"},
    "event_type": {"type": "string", "enum": ["page_view", "click", "purchase"]},
    "timestamp": {"type": "string", "format": "date-time"},
    "properties": {"type": "object"}
  }
}

Step 2: Create Kafka Topic

# Create topic with appropriate partitions
kafka-topics.sh --create \
  --bootstrap-server localhost:9092 \
  --topic user-events \
  --partitions 12 \
  --replication-factor 3 \
  --config retention.ms=604800000 \
  --config cleanup.policy=delete

# Verify topic
kafka-topics.sh --describe \
  --bootstrap-server localhost:9092 \
  --topic user-events

Step 3: Implement Spark Streaming Job

# streaming/user_events_processor.py
from pyspark.sql import SparkSession
from pyspark.sql.functions import (
    from_json, col, window, count, avg,
    to_timestamp, current_timestamp
)
from pyspark.sql.types import (
    StructType, StructField, StringType,
    TimestampType, MapType
)

# Initialize Spark
spark = SparkSession.builder \
    .appName("UserEventsProcessor") \
    .config("spark.sql.streaming.checkpointLocation", "/checkpoints/user-events") \
    .config("spark.sql.shuffle.partitions", "12") \
    .getOrCreate()

# Define schema
event_schema = StructType([
    StructField("event_id", StringType(), False),
    StructField("user_id", StringType(), False),
    StructField("event_type", StringType(), False),
    StructField("timestamp", StringType(), False),
    StructField("properties", MapType(StringType(), StringType()), True)
])

# Read from Kafka
events_df = spark.readStream \
    .format("kafka") \
    .option("kafka.bootstrap.servers", "localhost:9092") \
    .option("subscribe", "user-events") \
    .option("startingOffsets", "latest") \
    .option("failOnDataLoss", "false") \
    .load()

# Parse JSON
parsed_df = events_df \
    .select(from_json(col("value").cast("string"), event_schema).alias("data")) \
    .select("data.*") \
    .withColumn("event_timestamp", to_timestamp(col("timestamp")))

# Windowed aggregation
aggregated_df = parsed_df \
    .withWatermark("event_timestamp", "10 minutes") \
    .groupBy(
        window(col("event_timestamp"), "5 minutes"),
        col("event_type")
    ) \
    .agg(
        count("*").alias("event_count"),
        approx_count_distinct("user_id").alias("unique_users")
    )

# Write to Delta Lake
query = aggregated_df.writeStream \
    .format("delta") \
    .outputMode("append") \
    .option("checkpointLocation", "/checkpoints/user-events-aggregated") \
    .option("path", "/data/lake/user_events_aggregated") \
    .trigger(processingTime="1 minute") \
    .start()

query.awaitTermination()

Step 4: Handle Late Data and Errors

# Dead letter queue for failed records
from pyspark.sql.functions import current_timestamp, lit

def process_with_error_handling(batch_df, batch_id):
    try:
        # Attempt processing
        valid_df = batch_df.filter(col("event_id").isNotNull())
        invalid_df = batch_df.filter(col("event_id").isNull())

        # Write valid records
        valid_df.write \
            .format("delta") \
            .mode("append") \
            .save("/data/lake/user_events")

        # Write invalid to DLQ
        if invalid_df.count() > 0:
            invalid_df \
                .withColumn("error_timestamp", current_timestamp()) \
                .withColumn("error_reason", lit("missing_event_id")) \
                .write \
                .format("delta") \
                .mode("append") \
                .save("/data/lake/dlq/user_events")

    except Exception as e:
        # Log error, alert, continue
        logger.error(f"Batch {batch_id} failed: {e}")
        raise

# Use foreachBatch for custom processing
query = parsed_df.writeStream \
    .foreachBatch(process_with_error_handling) \
    .option("checkpointLocation", "/checkpoints/user-events") \
    .start()

Step 5: Monitor Stream Health

# monitoring/stream_metrics.py
from prometheus_client import Gauge, Counter, start_http_server

# Define metrics
RECORDS_PROCESSED = Counter(
    'stream_records_processed_total',
    'Total records processed',
    ['stream_name', 'status']
)

PROCESSING_LAG = Gauge(
    'stream_processing_lag_seconds',
    'Current processing lag',
    ['stream_name']
)

BATCH_DURATION = Gauge(
    'stream_batch_duration_seconds',
    'Last batch processing duration',
    ['stream_name']
)

def emit_metrics(query):
    """Emit Prometheus metrics from streaming query."""
    progress = query.lastProgress
    if progress:
        RECORDS_PROCESSED.labels(
            stream_name='user-events',
            status='success'
        ).inc(progress['numInputRows'])

        if progress['sources']:
            # Calculate lag from latest offset
            for source in progress['sources']:
                end_offset = source.get('endOffset', {})
                # Parse Kafka offsets and calculate lag

---

Workflow 3: Data Quality Framework Setup

Scenario: Implement comprehensive data quality monitoring with Great Expectations.

Step 1: Initialize Great Expectations

# Install and initialize
pip install great_expectations

great_expectations init

# Connect to data source
great_expectations datasource new

Step 2: Create Expectation Suite

# expectations/orders_suite.py
import great_expectations as gx

context = gx.get_context()

# Create expectation suite
suite = context.add_expectation_suite("orders_quality_suite")

# Add expectations
validator = context.get_validator(
    batch_request={
        "datasource_name": "warehouse",
        "data_asset_name": "orders",
    },
    expectation_suite_name="orders_quality_suite"
)

# Schema expectations
validator.expect_table_columns_to_match_ordered_list(
    column_list=[
        "order_id", "customer_id", "order_date",
        "total_amount", "status", "created_at"
    ]
)

# Completeness expectations
validator.expect_column_values_to_not_be_null("order_id")
validator.expect_column_values_to_not_be_null("customer_id")
validator.expect_column_values_to_not_be_null("order_date")

# Uniqueness expectations
validator.expect_column_values_to_be_unique("order_id")

# Range expectations
validator.expect_column_values_to_be_between(
    "total_amount",
    min_value=0,
    max_value=1000000
)

# Categorical expectations
validator.expect_column_values_to_be_in_set(
    "status",
    ["pending", "confirmed", "shipped", "delivered", "cancelled"]
)

# Freshness expectation
validator.expect_column_max_to_be_between(
    "order_date",
    min_value={"$PARAMETER": "now - timedelta(days=1)"},
    max_value={"$PARAMETER": "now"}
)

# Referential integrity
validator.expect_column_values_to_be_in_set(
    "customer_id",
    value_set={"$PARAMETER": "valid_customer_ids"}
)

validator.save_expectation_suite(discard_failed_expectations=False)

Step 3: Create Data Quality Checks with dbt

# models/marts/schema.yml
version: 2

models:
  - name: fct_orders
    description: "Order fact table with data quality checks"

    tests:
      # Row count check
      - dbt_utils.equal_rowcount:
          compare_model: ref('stg_orders')

      # Freshness check
      - dbt_utils.recency:
          datepart: hour
          field: created_at
          interval: 24

    columns:
      - name: order_id
        description: "Unique order identifier"
        tests:
          - unique
          - not_null
          - relationships:
              to: ref('dim_orders')
              field: order_id

      - name: total_amount
        tests:
          - not_null
          - dbt_utils.accepted_range:
              min_value: 0
              max_value: 1000000
              inclusive: true
          - dbt_expectations.expect_column_values_to_be_between:
              min_value: 0
              row_condition: "status != 'cancelled'"

      - name: customer_id
        tests:
          - not_null
          - relationships:
              to: ref('dim_customers')
              field: customer_id
              severity: warn

Step 4: Implement Data Contracts

# contracts/orders_contract.yaml
contract:
  name: orders_data_contract
  version: "1.0.0"
  owner: data-team@company.com

schema:
  type: object
  properties:
    order_id:
      type: string
      format: uuid
      description: "Unique order identifier"
    customer_id:
      type: string
      not_null: true
    order_date:
      type: date
      not_null: true
    total_amount:
      type: decimal
      precision: 10
      scale: 2
      minimum: 0
    status:
      type: string
      enum: ["pending", "confirmed", "shipped", "delivered", "cancelled"]

sla:
  freshness:
    max_delay_hours: 1
  completeness:
    min_percentage: 99.9
  accuracy:
    duplicate_tolerance: 0.01

consumers:
  - name: analytics-team
    usage: "Daily reporting dashboards"
  - name: ml-team
    usage: "Churn prediction model"

Step 5: Set Up Quality Monitoring Dashboard

# monitoring/quality_dashboard.py
from datetime import datetime, timedelta
import pandas as pd

def generate_quality_report(connection, table_name: str) -> dict:
    """Generate comprehensive data quality report."""

    report = {
        "table": table_name,
        "timestamp": datetime.now().isoformat(),
        "checks": {}
    }

    # Row count check
    row_count = connection.execute(
        f"SELECT COUNT(*) FROM {table_name}"
    ).fetchone()[0]
    report["checks"]["row_count"] = {
        "value": row_count,
        "status": "pass" if row_count > 0 else "fail"
    }

    # Freshness check
    max_date = connection.execute(
        f"SELECT MAX(created_at) FROM {table_name}"
    ).fetchone()[0]
    hours_old = (datetime.now() - max_date).total_seconds() / 3600
    report["checks"]["freshness"] = {
        "max_timestamp": max_date.isoformat(),
        "hours_old": round(hours_old, 2),
        "status": "pass" if hours_old < 24 else "fail"
    }

    # Null rate check
    null_query = f"""
    SELECT
        SUM(CASE WHEN order_id IS NULL THEN 1 ELSE 0 END) as null_order_id,
        SUM(CASE WHEN customer_id IS NULL THEN 1 ELSE 0 END) as null_customer_id,
        COUNT(*) as total
    FROM {table_name}
    """
    null_result = connection.execute(null_query).fetchone()
    report["checks"]["null_rates"] = {
        "order_id": null_result[0] / null_result[2] if null_result[2] > 0 else 0,
        "customer_id": null_result[1] / null_result[2] if null_result[2] > 0 else 0,
        "status": "pass" if null_result[0] == 0 and null_result[1] == 0 else "fail"
    }

    # Duplicate check
    dup_query = f"""
    SELECT COUNT(*) - COUNT(DISTINCT order_id) as duplicates
    FROM {table_name}
    """
    duplicates = connection.execute(dup_query).fetchone()[0]
    report["checks"]["duplicates"] = {
        "count": duplicates,
        "status": "pass" if duplicates == 0 else "fail"
    }

    # Overall status
    all_passed = all(
        check["status"] == "pass"
        for check in report["checks"].values()
    )
    report["overall_status"] = "pass" if all_passed else "fail"

    return report

---

Architecture Decision Framework

Use this framework to choose the right approach for your data pipeline.

Batch vs Streaming

Criteria	Batch	Streaming
Latency requirement	Hours to days	Seconds to minutes
Data volume	Large historical datasets	Continuous event streams
Processing complexity	Complex transformations, ML	Simple aggregations, filtering
Cost sensitivity	More cost-effective	Higher infrastructure cost
Error handling	Easier to reprocess	Requires careful design

Decision Tree:

Is real-time insight required?
├── Yes → Use streaming
│   └── Is exactly-once semantics needed?
│       ├── Yes → Kafka + Flink/Spark Structured Streaming
│       └── No → Kafka + consumer groups
└── No → Use batch
    └── Is data volume > 1TB daily?
        ├── Yes → Spark/Databricks
        └── No → dbt + warehouse compute

Lambda vs Kappa Architecture

Aspect	Lambda	Kappa
Complexity	Two codebases (batch + stream)	Single codebase
Maintenance	Higher (sync batch/stream logic)	Lower
Reprocessing	Native batch layer	Replay from source
Use case	ML training + real-time serving	Pure event-driven

When to choose Lambda:

• Need to train ML models on historical data
• Complex batch transformations not feasible in streaming
• Existing batch infrastructure

When to choose Kappa:

• Event-sourced architecture
• All processing can be expressed as stream operations
• Starting fresh without legacy systems

Data Warehouse vs Data Lakehouse

Feature	Warehouse (Snowflake/BigQuery)	Lakehouse (Delta/Iceberg)
Best for	BI, SQL analytics	ML, unstructured data
Storage cost	Higher (proprietary format)	Lower (open formats)
Flexibility	Schema-on-write	Schema-on-read
Performance	Excellent for SQL	Good, improving
Ecosystem	Mature BI tools	Growing ML tooling

---

Tech Stack

Category	Technologies
Languages	Python, SQL, Scala
Orchestration	Airflow, Prefect, Dagster
Transformation	dbt, Spark, Flink
Streaming	Kafka, Kinesis, Pub/Sub
Storage	S3, GCS, Delta Lake, Iceberg
Warehouses	Snowflake, BigQuery, Redshift, Databricks
Quality	Great Expectations, dbt tests, Monte Carlo
Monitoring	Prometheus, Grafana, Datadog

---

Reference Documentation

1. Data Pipeline Architecture

See references/data_pipeline_architecture.md for:

• Lambda vs Kappa architecture patterns
• Batch processing with Spark and Airflow
• Stream processing with Kafka and Flink
• Exactly-once semantics implementation
• Error handling and dead letter queues

2. Data Modeling Patterns

See references/data_modeling_patterns.md for:

• Dimensional modeling (Star/Snowflake)
• Slowly Changing Dimensions (SCD Types 1-6)
• Data Vault modeling
• dbt best practices
• Partitioning and clustering

3. DataOps Best Practices

See references/dataops_best_practices.md for:

• Data testing frameworks
• Data contracts and schema validation
• CI/CD for data pipelines
• Observability and lineage
• Incident response

---

Troubleshooting

Pipeline Failures

Symptom: Airflow DAG fails with timeout

Task exceeded max execution time

Solution:

1. Check resource allocation
2. Profile slow operations
3. Add incremental processing

# Increase timeout
default_args = {
    'execution_timeout': timedelta(hours=2),
}

# Or use incremental loads
WHERE updated_at > '{{ prev_ds }}'

---

Symptom: Spark job OOM

java.lang.OutOfMemoryError: Java heap space

Solution:

1. Increase executor memory
2. Reduce partition size
3. Use disk spill

spark.conf.set("spark.executor.memory", "8g")
spark.conf.set("spark.sql.shuffle.partitions", "200")
spark.conf.set("spark.memory.fraction", "0.8")

---

Symptom: Kafka consumer lag increasing

Consumer lag: 1000000 messages

Solution:

1. Increase consumer parallelism
2. Optimize processing logic
3. Scale consumer group

# Add more partitions
kafka-topics.sh --alter \
  --bootstrap-server localhost:9092 \
  --topic user-events \
  --partitions 24

---

Data Quality Issues

Symptom: Duplicate records appearing

Expected unique, found 150 duplicates

Solution:

1. Add deduplication logic
2. Use merge/upsert operations

-- dbt incremental with dedup
{{
    config(
        materialized='incremental',
        unique_key='order_id'
    )
}}

SELECT * FROM (
    SELECT
        *,
        ROW_NUMBER() OVER (
            PARTITION BY order_id
            ORDER BY updated_at DESC
        ) as rn
    FROM {{ source('raw', 'orders') }}
) WHERE rn = 1

---

Symptom: Stale data in tables

Last update: 3 days ago

Solution:

1. Check upstream pipeline status
2. Verify source availability
3. Add freshness monitoring

# dbt freshness check
sources:
  - name: raw
    freshness:
      warn_after: {count: 12, period: hour}
      error_after: {count: 24, period: hour}
    loaded_at_field: _loaded_at

---

Symptom: Schema drift detected

Column 'new_field' not in expected schema

Solution:

1. Update data contract
2. Modify transformations
3. Communicate with producers

# Handle schema evolution
df = spark.read.format("delta") \
    .option("mergeSchema", "true") \
    .load("/data/orders")

---

Performance Issues

Symptom: Query takes hours

Query runtime: 4 hours (expected: 30 minutes)

Solution:

1. Check query plan
2. Add proper partitioning
3. Optimize joins

-- Before: Full table scan
SELECT * FROM orders WHERE order_date = '2024-01-15';

-- After: Partition pruning
-- Table partitioned by order_date
SELECT * FROM orders WHERE order_date = '2024-01-15';

-- Add clustering for frequent filters
ALTER TABLE orders CLUSTER BY (customer_id);

---

Symptom: dbt model takes too long

Model fct_orders completed in 45 minutes

Solution:

1. Use incremental materialization
2. Reduce upstream dependencies
3. Pre-aggregate where possible

-- Convert to incremental
{{
    config(
        materialized='incremental',
        unique_key='order_id',
        on_schema_change='sync_all_columns'
    )
}}

SELECT * FROM {{ ref('stg_orders') }}
{% if is_incremental() %}
WHERE _loaded_at > (SELECT MAX(_loaded_at) FROM {{ this }})
{% endif %}