Senior Data Engineer

Baker Tilly; Bengaluru; On-site; INR 1,200,000 - 3,000,000; Full time

Job summary

Baker Tilly seeks a Data Engineer to design and operate Azure Databricks‑based pipelines, ingesting diverse data and enabling AI/ML readiness across the organization.

The role emphasizes governance with Unity Catalog, Delta Lake modeling, and cross‑cloud data movement, delivering analytics-ready datasets for Power BI/Tableau and enterprise BI initiatives.

Strong experience with Spark, PySpark, SQL, and medallion-architecture pipelines is required, with 5–8 years in data engineering.

Qualifications

• Bachelor’s degree in IT, CS, Data Science, Analytics, or Statistics required.
• 5-8 years of hands-on experience in data engineering, data integration, or analytics engineering with Databricks.
• Deep, hands-on experience with Azure Databricks including workspaces, clusters/SQL warehouses, Unity Catalog, and ADLS Gen2 integration.
• Strong proficiency in Spark/PySpark and SQL for complex queries and data transformations.
• Proven experience building medallion-architecture pipelines (bronze -> silver -> gold -> platinum) on Delta Lake.
• Hands-on experience ingesting structured, semi-structured, and unstructured data via Spark Structured Streaming, connectors, Databricks Marketplace, and Delta Sharing.
• Practical experience with Unity Catalog governance, including access control, lineage, and data classification.
• Experience migrating or sharing data across cloud platforms (AWS, GCP, Snowflake, Redshift, BigQuery) into a Databricks lakehouse.

Responsibilities

• Design, build, and maintain robust ETL/ELT pipelines on Azure Databricks to ingest, transform, and curate data across the medallion architecture from raw to consumption-ready layers.
• Ingest data from diverse sources using streaming pipelines (Spark Structured Streaming, Auto Loader), connectors, Databricks Marketplace, and Delta Sharing.
• Build and manage Unity Catalog assets (catalogs, schemas, tables, and Volumes) and configure external locations/Volumes for governed access to ADLS Gen2.
• Design and implement audit, control, and operational-metadata tables for end-to-end observability and reconciliation.
• Define and apply data governance strategies across the lakehouse, including access control, data classification, PII handling, and retention.
• Develop reusable Delta Lake data models optimized for AI/ML, analytics, and reporting; apply partitioning, Z-ordering, and OPTIMIZE.
• Prepare analytics- and AI-ready datasets for upstream AI/ML activities and feature engineering.
• Enable cross-cloud data movement and sharing into Azure Databricks lakehouse via Delta Sharing.
• Troubleshoot data pipeline and data-quality issues; collaborate with BI and analytics teams to deliver curated datasets for Power BI/Tableau.
• Orchestrate workflows with Databricks Workflows/Jobs and DLT; support CI/CD with Git and Asset Bundles; contribute to standards.

Skills

Azure Databricks

Spark/PySpark

SQL

Delta Lake

Unity Catalog

ADLS Gen2

ETL/ELT pipelines

CI/CD

Databricks Workflows/Jobs

DLT

Education

Bachelor’s degree

Tools

Lakeflow Connect

Databricks Marketplace

Delta Sharing

ADLS Gen2 integration

JDBC/CDC

Power BI/Tableau integration

Job description

Overview

BTVK Advisory is a leading advisory firm whose specialized professionals guide clients through an ever-changing business world, helping them win now and anticipate tomorrow. BTVK Advisory, and its affiliated entities, have operations in North America, South America, Europe, Asia, and Australia. BTVK Advisory’s ultimate parent entity, Baker Tilly US, LLP, is an independent member of Baker Tilly International, a worldwide network of independent accounting and business advisory firms in 141 territories, with 43,000 professionals and a combined worldwide revenue of $5.2 billion.

Baker Tilly is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability or protected veteran status, gender identity, sexual orientation, or any other legally protected basis, in accordance with applicable federal, state or local law.

Job Description:

Responsibilities:

• Design, build, and maintain robust, scalable ETL/ELT pipelines on Azure Databricks to ingest, transform, and curate data across the medallion architecture — from raw/bronze landing through silver and gold to platinum/consumption-ready layers — ensuring reliability, performance, and timeliness.
• Ingest structured, semi-structured, and unstructured data from a wide range of source systems using custom streaming pipelines (Spark Structured Streaming, Auto Loader), native and partner connectors (Lakeflow Connect, JDBC/CDC), Databricks Marketplace, and Delta Sharing, following proper governance.
• Build and manage Unity Catalog assets (catalogs, schemas, tables, and Volumes) and configure or mount external locations/Volumes for governed access to cloud storage such as ADLS Gen2.
• Design and implement audit, control, and operational-metadata tables that capture pipeline run history, data lineage, record counts, data-quality outcomes, and exception handling, enabling end-to-end observability, reconciliation, and auditability.
• Define and apply data governance strategies across the lakehouse — including access control, data classification, PII handling, lineage, retention, and data-quality frameworks — leveraging Unity Catalog and aligned to enterprise standards.
• Develop reusable, performant Delta Lake data models optimized for downstream AI/ML, analytics, and reporting consumption, applying techniques such as partitioning, Z-ordering / liquid clustering, and OPTIMIZE.
• Prepare and structure analytics- and AI-ready datasets with upstream AI/ML activities in mind — supporting feature engineering, feature stores, embeddings/vector data, and ML-ready data products for the organization’s AI initiatives.
• Enable cross-cloud data movement and sharing — migrating or sharing data from other cloud platforms (e.g., AWS, GCP) and cloud data warehouses into the Azure Databricks lakehouse, including via Delta Sharing.
• Troubleshoot and resolve data pipeline and data-quality issues to ensure consistent, dependable data delivery, and partner with BI, analytics, and business teams to deliver curated, analytics-ready datasets for Power BI and Tableau.
• Orchestrate and automate workflows using Databricks Workflows/Jobs and Lakeflow Declarative Pipelines (DLT), with CI/CD via Git and Databricks Asset Bundles; contribute to data engineering standards, documentation, code reviews, testing, and continuous improvement.
• Deliver projects using Agile or hybrid methodologies, manage tasks independently, provide accurate status updates, and proactively identify risks or dependencies.

Qualifications:

• Bachelor’s degree required, preferably in Information Technology, Computer Science, Data Science, Analytics, or Statistics.
• 5-8 years of hands‑on experience in data engineering, data integration, or analytics engineering, including substantial time delivering on Databricks.
• Deep, hands‑on experience with Azure Databricks and its critical components, including workspaces, clusters/SQL warehouses, Unity Catalog, and ADLS Gen2 integration.
• Strong proficiency in Spark/PySpark and SQL, including the ability to develop complex queries and perform advanced data transformations.
• Proven experience building medallion‑architecture pipelines (bronze -> silver -> gold -> platinum) on Delta Lake.
• Hands‑on experience ingesting structured, semi‑structured, and unstructured data via custom streams (Structured Streaming / Auto Loader), connectors, Databricks Marketplace, and Delta Sharing.
• Practical experience with Unity Catalog governance, including access control, lineage, data classification, and Volumes/external locations.
• Experience designing audit/control and operational‑metadata tables and data-quality frameworks for pipeline observability and reconciliation.
• Experience migrating or sharing data across cloud platforms and modern data ecosystems (e.g., AWS, GCP, Snowflake, Redshift, BigQuery) into a Databricks lakehouse.
• Familiarity with data orchestration (Databricks Workflows/Jobs, DLT) and CI/CD practices (Git, Databricks Asset Bundles) within data engineering environments.
• Solid understanding of data governance, data-quality frameworks, and metric standardization, with experience supporting AI, advanced analytics, or automation initiatives, coupled with strong analytical and problem‑solving skills.

Preferred / Bonus Qualifications:

• Experience handling tax, assurance, and financial data, including familiarity with the sensitivity, confidentiality, and regulatory considerations associated with such data.
• Exposure to AI/ML enablement on Databricks (e.g., MLflow, feature stores, Mosaic AI, Vector Search) and an understanding of how curated data feeds these workloads.
• Hands‑on experience with Power BI and/or Tableau data modeling and semantic‑layer design for downstream consumption.
• Relevant Databricks certification (e.g., Databricks Certified Data Engineer Professional).